The Power of Scale for Parameter-Efficient Prompt Tuning

Brian Lester; Noah Constant; Rami Al-Rfou

REVIEW 1 major objections 3 minor 212 cited by

The Power of Scale for Parameter-Efficient Prompt Tuning

T0 review · 1 major / 3 minor · reviewed 2026-05-11 · grok-4.3

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read As models grow to billions of parameters, learning a small set of soft prompts matches the performance of tuning all model weights while keeping the base model frozen.

desk verdict Prompt tuning matches full tuning at large T5 scale, and the scaling curves are the useful new observation. read the letter →

arxiv 2104.08691 v2 pith:WF25QMNX submitted 2021-04-18 cs.CL

Brian Lester , Rami Al-Rfou , Noah Constant This is my paper · ORCID

classification cs.CL

keywords prompttuningsoftpromptsparameter-efficientlearninglanguagemodelscalingT5domaintransferfrozenmodels

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper examines prompt tuning, a method that learns continuous soft prompts through backpropagation to condition a frozen language model for specific tasks. Experiments on T5 models show that this approach grows more competitive with full model tuning as scale increases, eventually closing the performance gap at large sizes. This enables reuse of one shared model across many tasks without updating its weights. The technique also yields greater robustness under domain shifts than full tuning and simplifies earlier prefix-tuning methods while beating GPT-3 few-shot baselines.

What carries the argument

Soft prompts: a small set of continuous, trainable vectors optimized via gradient descent to condition the input of a frozen language model.

What would settle it

Prompt tuning failing to match full model tuning performance on a new family of models larger than a few billion parameters using the same training procedure.

Watch

Extended reading notes

Core claim

Prompt tuning closes the gap with model tuning at large scales: as T5 models exceed billions of parameters, learned soft prompts achieve performance comparable to tuning all model weights, while remaining far more parameter-efficient and enabling the same frozen model to serve multiple downstream tasks.

Load-bearing premise

The scaling trend observed on T5 models and the tested tasks will hold for other model families, architectures, and task distributions.

Editorial extensions

If this is right

One frozen model can be reused for many tasks by storing only the small prompt parameters instead of separate full copies.
Serving costs drop because the large model weights need to be loaded only once and shared across applications.
Domain-transfer robustness improves relative to full model tuning.
The approach simplifies prefix tuning while matching its results on the evaluated settings.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Deployment pipelines for very large models can shift toward storing and swapping small prompts rather than full fine-tuned weights.
If the trend continues, parameter-efficient adaptation may become the default route for applying foundation models to new tasks.
The method invites direct comparisons on non-T5 architectures to test whether the scale advantage is architecture-specific.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Desk Editor's Note

read the letter

The main takeaway is that a learned soft prompt on a frozen T5 model closes the performance gap to full model tuning once the backbone exceeds a few billion parameters. They demonstrate this with size ablations from 60M to 11B parameters and show the method also beats GPT-3 few-shot results while using far fewer trainable parameters. The approach is presented as a simplification of prefix tuning, dropping the per-layer prefixes in favor of a single prompt layer at the input. That reduction is clean and the direct comparisons to prefix tuning and other baselines are helpful. The robustness claim on domain transfer is a secondary but positive note. The experiments follow consistent protocols across model sizes and include prompt-length sweeps, which strengthens the scaling story. No circularity or obvious fitting issues appear in the reported setup. The limitation is that all results stay within the T5 family on standard classification and generation tasks. We lack data on decoder-only models or harder distributions, so the generalization of the scaling trend remains an open question rather than a proven law. The abstract does not report run-to-run variance or formal significance tests, but the trend direction is consistent enough that this does not undermine the central observation. This paper is for groups working on efficient adaptation of large language models who need a lightweight alternative to full fine-tuning or storing multiple copies. It is worth a serious referee because the empirical scaling result is reproducible in principle, addresses a practical cost issue, and the method is simple enough to test quickly. I would send it out for review.

Referee Report

1 major / 3 minor

Summary. The paper introduces prompt tuning, a parameter-efficient adaptation method that learns a small number of continuous 'soft prompt' embeddings while keeping the underlying language model (T5) frozen. It reports that on GLUE and SuperGLUE tasks, prompt tuning's performance gap to full model tuning shrinks with scale; at 11B parameters the two methods become competitive, and prompt tuning also outperforms GPT-3 few-shot learning while showing improved robustness under domain shift.

Significance. If the reported scaling trend holds, the result is significant: it demonstrates that a single frozen model can be reused across many tasks via tiny per-task prompt parameters, substantially lowering storage and serving costs for large LMs. The systematic size ablations on T5 (60M–11B) and direct comparisons to prefix tuning constitute a clear empirical contribution.

major comments (1)

[§4.2 and Table 1] §4.2 and Table 1: the central claim that prompt tuning 'matches' model tuning at 11B parameters rests on point estimates; no standard deviations across random seeds or statistical significance tests are reported, which weakens the assertion that the gap has closed rather than narrowed within noise.

minor comments (3)

[§3.1] §3.1: the definition of the soft prompt as a sequence of length k is clear, but the initialization scheme (random vs. vocabulary tokens) and whether it is held constant across all model sizes should be stated explicitly in the main text rather than only in the appendix.
[Figure 3] Figure 3: axis labels and legend entries are too small for print; the scaling curves would be easier to read if the x-axis were log-scaled with explicit parameter counts annotated.
[§5] §5: the discussion of domain-transfer robustness would benefit from a brief statement of how the source and target domains were selected and whether the improvement is consistent across all transfer pairs or driven by a subset.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the positive evaluation of our work and the recommendation for minor revision. We address the single major comment below.

read point-by-point responses

Referee: [§4.2 and Table 1] §4.2 and Table 1: the central claim that prompt tuning 'matches' model tuning at 11B parameters rests on point estimates; no standard deviations across random seeds or statistical significance tests are reported, which weakens the assertion that the gap has closed rather than narrowed within noise.

Authors: We agree that reporting variability across random seeds and including statistical significance tests would make the central claim more robust. Our original experiments used single runs for the 11B models owing to the substantial computational cost of training and evaluating models at this scale. In the revised manuscript, we will rerun the 11B-scale experiments with multiple random seeds, report mean performance and standard deviations in Table 1 and §4.2, and add a brief discussion of statistical significance for the key comparisons. This will allow readers to assess whether the performance gap has closed within the observed variance. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: empirical scaling observations only

full rationale

The paper reports direct experimental comparisons of prompt tuning versus model tuning across T5 model sizes (60M to 11B parameters) on standard NLP tasks. No equations, fitted parameters, or predictions are defined in terms of the target metrics; performance gaps are measured on held-out test sets using fixed training protocols. The central claim is an observed trend, not a derivation. Self-citations are absent from load-bearing steps, and the prefix-tuning comparison cites external work (Li & Liang 2021) without circular reduction. The result is self-contained against external benchmarks.

Assumptions & free parameters 1 free parameters · 1 assumptions · 1 invented entities

The approach rests on standard transfer-learning assumptions for language models plus the empirical observation of scaling. No load-bearing free parameters or invented entities are required beyond the soft prompt vectors themselves.

free parameters (1)

soft prompt length
The number of tokens in the learned prompt is a hyperparameter selected for each experiment.

assumptions (1)

domain assumption A frozen pre-trained language model encodes sufficient general knowledge that task-specific behavior can be elicited by conditioning on a small learned input prefix.
Invoked throughout the method description and scaling experiments.

invented entities (1)

soft prompt
purpose: Continuous vector prefix that conditions the frozen model for a downstream task.
The central new mechanism introduced and optimized via backpropagation.

how reviews work

0 comments

Cite this review

Pith. "Pith review of The Power of Scale for Parameter-Efficient Prompt Tuning." pith.science (2026). https://pith.science/paper/WF25QMNX

@misc{pith2026210408691,
  author       = {Pith},
  title        = {Pith review of: The Power of Scale for Parameter-Efficient Prompt Tuning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WF25QMNX}},
  note         = {Machine review of arXiv:2104.08691}
}

read the original abstract

In this work, we explore "prompt tuning", a simple yet effective mechanism for learning "soft prompts" to condition frozen language models to perform specific downstream tasks. Unlike the discrete text prompts used by GPT-3, soft prompts are learned through backpropagation and can be tuned to incorporate signal from any number of labeled examples. Our end-to-end learned approach outperforms GPT-3's "few-shot" learning by a large margin. More remarkably, through ablations on model size using T5, we show that prompt tuning becomes more competitive with scale: as models exceed billions of parameters, our method "closes the gap" and matches the strong performance of model tuning (where all model weights are tuned). This finding is especially relevant in that large models are costly to share and serve, and the ability to reuse one frozen model for multiple downstream tasks can ease this burden. Our method can be seen as a simplification of the recently proposed "prefix tuning" of Li and Liang (2021), and we provide a comparison to this and other similar approaches. Finally, we show that conditioning a frozen model with soft prompts confers benefits in robustness to domain transfer, as compared to full model tuning.

Discussion (0). Continue with ORCID to comment.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith.Cost.FunctionalEquation washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

through ablations on model size using T5, we show that prompt tuning becomes more competitive with scale: as models exceed billions of parameters, our method closes the gap and matches the strong performance of model tuning
IndisputableMonolith.Foundation.HierarchyEmergence hierarchy_emergence_forces_phi unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

prompt tuning becomes more competitive with scale

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Forward citations

Showing 60 of 212 Pith papers that cite this

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. See all 212 Pith citations

Exposing Functional Fusion: A New Class of Strategic Backdoor in Dynamic Prompt Architectures
cs.CR 2026-05 unverdicted novelty 8.0 of 10

VIPER exposes Functional Fusion in dynamic prompt architectures, enabling a backdoor that resists pruning by tightly integrating attack and utility parameters in the same high-magnitude core.
Universal and Transferable Adversarial Attacks on Aligned Language Models
cs.CL 2023-07 accept novelty 8.0 of 10

Gradient and greedy search over token suffixes produces universal, transferable adversarial prompts that elicit objectionable outputs from aligned models including black-box commercial systems.
Structure Before Collapse: Transient semantic geometry in next-token prediction
cs.LG 2026-06 unverdicted novelty 7.0 of 10

Semantic geometry emerges transiently early in next-token prediction training before collapsing to Neural Collapse symmetry in synthetic settings with latent semantic factors.
Fine-tuning Multi-modal LLMs with ART: Art-based Reinforcement Training
cs.LG 2026-06 unverdicted novelty 7.0 of 10

ART optimizes visual pixel inputs to frozen MLLMs to achieve LoRA-competitive accuracy on math and structured tool-use benchmarks without modifying computational graphs.
Parameter-Efficient Fine-Tuning with Learnable Rank
cs.CL 2026-06 unverdicted novelty 7.0 of 10

LR-LoRA learns per-layer adapter ranks during training and reports outperforming fixed-rank LoRA and other PEFT baselines on language understanding and commonsense reasoning tasks.
Remembering by Reconstructing: Domain Incremental Learning With Test-Time Training on Video Streams
cs.CV 2026-05 unverdicted novelty 7.0 of 10

Domain-incremental video learning that permits forgetting through per-domain LoRA adapters and recovers the matching adapter at inference via test-time training on a self-supervised MAE reconstruction head.
Beyond Detection: A Structure-Aware Framework for Scene Text Tracking
cs.CV 2026-05 unverdicted novelty 7.0 of 10

SymTrack is the first systematic detection-free framework for scene text tracking that constructs benchmarks from video text spotting datasets and reports up to 11.97% AUC gains over prior trackers.
PragLocker: Protecting Agent Intellectual Property in Untrusted Deployments via Non-Portable Prompts
cs.CR 2026-05 unverdicted novelty 7.0 of 10

PragLocker protects agent prompts as IP by building non-portable obfuscated versions that function only on the intended LLM through code-symbol semantic anchoring followed by target-model feedback noise injection.
Rethinking Gating Mechanism in Sparse MoE: Handling Arbitrary Modality Inputs with Confidence-Guided Gate
cs.LG 2025-05 unverdicted novelty 7.0 of 10

ConfSMoE adds expert-opinion imputation and detaches softmax routing scores to ground-truth task confidence to relieve expert collapse in SMoE without extra load-balance losses, evaluated on four real-world datasets.
CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features
cs.CV 2025-05 conditional novelty 7.0 of 10

CSTrack proposes compact spatial and temporal feature modules for RGB-X tracking, reporting new state-of-the-art results on DepthTrack, VOT-RGBD2022, LasHeR, RGBT234, and VisEvent.
Deliberation in Latent Space via Differentiable Cache Augmentation
cs.CL 2024-12 conditional novelty 7.0 of 10

A frozen LLM's key-value cache can be augmented with latent embeddings from a separately trained coprocessor, lowering perplexity and improving reasoning benchmarks without task-specific training.
Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation
cs.CV 2023-10 unverdicted novelty 7.0 of 10

A new shared video-image tokenizer enables large language models to surpass diffusion models on standard visual generation benchmarks.
Efficient Memory Management for Large Language Model Serving with PagedAttention
cs.LG 2023-09 conditional novelty 7.0 of 10

PagedAttention achieves near-zero waste in LLM key-value cache memory and enables 2-4x higher serving throughput than prior systems.
Large Language Models as Optimizers
cs.LG 2023-09 unverdicted novelty 7.0 of 10

Large language models can optimize by being prompted with histories of past solutions and scores to propose better ones, producing prompts that raise accuracy up to 8% on GSM8K and 50% on Big-Bench Hard over human-des...
LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention
cs.CV 2023-03 conditional novelty 7.0 of 10

LLaMA-Adapter turns frozen LLaMA 7B into a capable instruction follower using only 1.2M new parameters and zero-init attention, matching Alpaca while extending to image-conditioned reasoning on ScienceQA and COCO.
Flamingo: a Visual Language Model for Few-Shot Learning
cs.CV 2022-04 unverdicted novelty 7.0 of 10

Flamingo models reach new state-of-the-art few-shot results on image and video tasks by bridging frozen vision and language models with cross-attention layers trained on interleaved web-scale data.
An Explanation of In-context Learning as Implicit Bayesian Inference
cs.CL 2021-11 conditional novelty 7.0 of 10

In-context learning emerges as implicit Bayesian inference of latent concepts when pretraining data has long-range coherence, proven for mixture-of-HMM distributions and replicated on the synthetic GINC dataset.
Multitask Prompted Training Enables Zero-Shot Task Generalization
cs.LG 2021-10 conditional novelty 7.0 of 10

Multitask fine-tuning of an encoder-decoder model on prompted datasets produces zero-shot generalization that often beats models up to 16 times larger on standard benchmarks.
\k{appa}-LoRA: Condition Numbers Reveal Which LoRA Matrices Worth Updating
cs.LG 2026-07 conditional novelty 6.0 of 10

Selecting the top 50% of LoRA weight matrices by condition number halves trainable parameters and cuts fine-tuning time by about 16% while roughly matching full-LoRA accuracy.
Test-Time Scaling via Error Localization
cs.LG 2026-07 conditional novelty 6.0 of 10

TTEL uses feedback-induced token probability drops to localize the first error in a failed reasoning trace and branch a new generation from that prefix, improving pass@k per token on coding and math benchmarks.
Logical Judgments Under Pressure: Diagnosing Syllogistic Stability with Learned Soft Prefixes
cs.AI 2026-07 conditional novelty 6.0 of 10

Learned soft prefixes reliably flip correct syllogistic judgments in LLMs, transferring across unseen forms and interfaces and behaving mainly as a broad answer preference rather than a transferable logical operation.
Chebyshev Manifold Adaptation
cs.LG 2026-07 conditional novelty 6.0 of 10

ChebyMA uses Chebyshev polynomial surfaces to parameterize weight updates, claiming a better parameter-accuracy trade-off than LoRA, TLoRA, and StelLA on CIFAR and text classification.
Toward Federated Multimodal Graph Foundation Models: A Topology-Aware Multimodal Alignment Framework
cs.LG 2026-07 conditional novelty 6.0 of 10

A federated pretraining-and-prompt-tuning framework that aligns image, text, and graph-topology information across privacy-separated clients claims consistent state-of-the-art results on 12 multimodal graph datasets.
CL-Anomaly: Layer-Adaptive Mixture-of-Experts with Multimodal Large Language Model for Continual Learning in Anomaly Detection
cs.CV 2026-07 conditional novelty 6.0 of 10

A private-plus-shared LoRA MoE with layer-adaptive momentum transfer enables continual anomaly detection on MLLMs and beats prior continual-learning baselines across class, domain, and modality shifts.
EPnG: Adaptive Expert Prune-and-Grow for Parameter-Efficient MoE Fine-tuning
cs.LG 2026-07 unverdicted novelty 6.0 of 10

EPnG reallocates LoRA capacity in MoE models by pruning experts with low router gate probabilities and expanding high-importance ones via rank growth, outperforming standard LoRA and nearing full fine-tuning performan...
Behavioral and Representational Evidence of Binomial Ordering Preferences in Large Language Models
cs.CL 2026-06 unverdicted novelty 6.0 of 10

LLMs recover dominant binomial orders from corpora but align less closely with exact preference distributions, with preference strength partially encoded in middle-to-late layers and manipulable via steering.
Substrate Asymmetry in User-Side Memory: A Diagnostic Framework
cs.CL 2026-06 unverdicted novelty 6.0 of 10

User memory in LLMs factors into three orthogonal axes where parametric adapters and retrieval show opposite strengths, with causal evidence from attention interventions and an alignment tax on RLHF models.
5% > 100%: Flatness Preference is All You Need for Multimodal Parameter-Efficient Fine-Tuning
cs.CV 2026-06 unverdicted novelty 6.0 of 10

Flatness Preference Optimization (FlatPO) improves multimodal PEFT generalization by flattening a small set of sharp dimensions that dominate performance.
Unveiling Privacy Risks in Multi-modal Large Language Models: Task-specific Vulnerabilities and Mitigation Challenges
cs.CR 2026-06 unverdicted novelty 6.0 of 10

Introduces MM-Privacy dataset and evaluations showing MLLMs leak sensitive data from images in various tasks, highlighting task inconsistency effects.
Language Models Need Sleep: Learning to Self-Modify and Consolidate Memories
cs.LG 2026-06 unverdicted novelty 6.0 of 10

Sleep-time Knowledge Seeding plus Dreaming lets LLMs expand capacity, distill fragile in-context memories into stable parameters, and self-improve without human labels.
Latent Diffusion Pretraining for Crystal Property Prediction
cs.LG 2026-05 unverdicted novelty 6.0 of 10

CrysLDNet combines VAE and latent diffusion pretraining on unlabeled crystals to improve graph encoder performance on property prediction by about 4-5% on JARVIS and MP datasets.
V-LynX: Token Interface Alignment for Video+X LLMs
cs.CV 2026-05 unverdicted novelty 6.0 of 10

V-LynX integrates novel modalities into frozen Video LLMs by aligning to an internalized continuous token manifold using unpaired unimodal data and attention/statistical matching.
TadA-Bench: A Million-Variant Benchmark for Future-Round Discovery Toward Agentic Protein Engineering
q-bio.QM 2026-05 unverdicted novelty 6.0 of 10

TadA-Bench supplies a chronological million-variant wet-lab replay benchmark from 31 TadA directed-evolution rounds that evaluates models on future-round variant ranking given only earlier data.
When Does Demographic Information Help? Data and Modeling Regimes for Perspective-Aware Hate Speech Detection
cs.CL 2026-05 unverdicted novelty 6.0 of 10

Demographic information aids perspective-aware hate speech detection in regimes of low training disagreement and high test disagreement, with a gated residual model proving effective on high-disagreement examples acro...
SurfDesign: Effective Protein Design on Molecular Surfaces
q-bio.BM 2026-05 unverdicted novelty 6.0 of 10

SurfDesign introduces surface-conditioned protein design via manifold modeling and equivariant message passing on surfaces integrated with pretrained language models, outperforming prior methods on binder and enzyme d...
MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models
cs.CV 2026-05 unverdicted novelty 6.0 of 10

MAIL++ embeds bidirectional multi-modal agent layers with meta bridges directly into VLM computation modules for improved few-shot classification and retrieval over prior PEFT approaches.
ACE-LoRA: Adaptive Orthogonal Decoupling for Continual Image Editing
cs.CV 2026-05 unverdicted novelty 6.0 of 10

ACE-LoRA introduces adaptive orthogonal decoupling and rank-invariant compression for continual image editing in diffusion models, plus the CIE-Bench benchmark.
Octopus: History-Free Gradient Orthogonalization for Continual Learning in Multimodal Large Language Models
cs.LG 2026-05 unverdicted novelty 6.0 of 10

Octopus introduces history-free gradient orthogonalization in a two-stage finetuning framework to achieve state-of-the-art continual learning results for multimodal LLMs on the UCIT benchmark.
PEML: Parameter-efficient Multi-Task Learning with Optimized Continuous Prompts
cs.CL 2026-05 unverdicted novelty 6.0 of 10

PEML co-optimizes continuous prompts and low-rank adaptations to deliver up to 6.67% average accuracy gains over existing multi-task PEFT methods on GLUE, SuperGLUE, and other benchmarks.
Combining pre-trained models via localized model averaging
stat.ME 2026-05 unverdicted novelty 6.0 of 10

Localized model averaging with covariate-dependent weights achieves asymptotic optimality and weight consistency for combining pre-trained models under a general loss framework.
Parameter Efficiency Is Not Memory Efficiency: Rethinking Fine-Tuning for On-Device LLM Adaptation
cs.LG 2026-04 unverdicted novelty 6.0 of 10

LARS constrains activation subspaces to decouple memory use from sequence length, cutting GPU memory by 33.5% and CPU memory by 52% versus LoRA while keeping accuracy comparable.
CoLA: Cross-Modal Low-rank Adaptation for Multimodal Downstream Tasks
cs.CV 2026-04 unverdicted novelty 6.0 of 10

CoLA introduces a dual-path low-rank adaptation method that adds cross-modal learning to LoRA, delivering small gains over standard LoRA on visual grounding and audio-visual benchmarks while preserving parameter efficiency.
DetPO: In-Context Learning with Multi-Modal LLMs for Few-Shot Object Detection
cs.CV 2026-03 conditional novelty 6.0 of 10

Detection Prompt Optimization (DetPO) improves few-shot object detection with black-box MLLMs by iteratively refining text prompts from TP/FP/FN errors on few-shot examples, gaining up to 9.7 mAP over prior black-box methods.
Rethinking LoRA for Privacy-Preserving Federated Learning in Large Models
cs.LG 2026-02 unverdicted novelty 6.0 of 10

LA-LoRA decouples LoRA matrix updates in DPFL settings to improve robustness to privacy noise, delivering up to 16.83% higher accuracy than prior LoRA variants on Swin-B under strict epsilon=1.
The Vision Wormhole: Latent-Space Communication in Heterogeneous Multi-Agent Systems
cs.CL 2026-02 conditional novelty 6.0 of 10

Reasoning messages between heterogeneous VLMs can be routed through the image-token span: a distilled universal codec plus affine alignment transmits latent traces across model families, cutting wall-clock time in sma...
Escaping the BLEU Trap: A Signal-Grounded Framework with Decoupled Semantic Guidance for EEG-to-Text Decoding
cs.CL 2026-02 unverdicted novelty 6.0 of 10

SemKey predicts four semantic attributes from EEG and conditions a frozen LLM on them, beating prior decoders on new semantic-alignment metrics while leaving true word-level accuracy low (2.7% content recall).
PLATE: Plasticity-Tunable Efficient Adapters for Geometry-Aware Continual Learning
cs.LG 2026-02 conditional novelty 6.0 of 10

PLATE constructs frozen weight-derived bases B and Q and trains only a small core A, reducing catastrophic forgetting during data-free continual fine-tuning.
Do Fine-Tuned LLMs Understand Vulnerabilities? An Investigation into the Semantic Trap
cs.CR 2026-01 unverdicted novelty 6.0 of 10

Fine-tuned decoder-only LLMs fall into a Semantic Trap on vulnerability detection, achieving high scores on unpaired normal code but failing on paired vulnerable-patched code, semantic perturbations, and gap analysis,...
Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles
cs.LG 2026-01 conditional novelty 6.0 of 10

Training only per-member singular values of pretrained weight matrices, while freezing singular vectors, produces an implicit ensemble with calibration near deep ensembles at <1% parameter overhead.
One Prompt, Many Sounds: Modeling Listener Variability in LLM-Based Equalization
cs.SD 2026-01 unverdicted novelty 6.0 of 10

LLMs using in-context learning and fine-tuning on listener experiment data generate equalization settings that align better with population preferences than random sampling or static presets.
Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models
cs.LG 2026-01 reject novelty 6.0 of 10

SALR combines static pruning of frozen weights with a trainable truncated-SVD low-rank residual adapter to match LoRA accuracy at 50% sparsity, cutting model size ~2x and giving ~1.7x inference speedup.
FlexAvatar: Learning Complete 3D Head Avatars with Partial Supervision
cs.CV 2025-12 unverdicted novelty 6.0 of 10

FlexAvatar introduces bias sinks in a transformer to unify monocular and multi-view training, yielding complete 3D head avatars with strong generalization and view extrapolation from single images.
Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation
cs.CV 2025-12 conditional novelty 6.0 of 10

DAPO learns shared defect-aware text prompts that let CLIP segment known and novel defect types in unseen industrial domains.
C-NAV: Towards Self-Evolving Continual Object Navigation in Open World
cs.RO 2025-10 unverdicted novelty 6.0 of 10

C-Nav is a continual visual navigation framework with dual-path anti-forgetting via feature distillation and replay plus adaptive sampling that outperforms baselines on a new continual object navigation benchmark whil...
MHA-RAG: Improving Efficiency, Accuracy, and Consistency by Encoding Exemplars as Soft Prompts
cs.AI 2025-10 conditional novelty 6.0 of 10

MHA-RAG encodes retrieved exemplars into order-invariant soft prompts via multi-head attention, claiming ~20-point effective-accuracy gains over RAG at ~10x lower inference FLOPs.
TAP: Parameter-efficient Task-Aware Prompting for Adverse Weather Removal
cs.CV 2025-08 conditional novelty 6.0 of 10

A two-stage prompt-tuning method with low-rank and contrastive prompt enhancement claims all-in-one adverse weather removal at 2.75M parameters.
NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding
cs.CV 2025-08 conditional novelty 6.0 of 10

NEARL-CLIP reports top accuracy on three medical image classification benchmarks by adding bidirectional cross-modal attention and orthogonalized adapters to CLIP with only 1.46M parameters.
RainbowPrompt: Diversity-Enhanced Prompt-Evolving for Continual Learning
cs.CV 2025-07 conditional novelty 6.0 of 10

RainbowPrompt evolves past and new prompts via attention and alignment before averaging them, beating several prompt-CL baselines on image and video class-incremental benchmarks.
Learning to Imitate with Less: Efficient Individual Behavior Modeling in Chess
cs.AI 2025-07 conditional novelty 6.0 of 10

Maia4All models individual chess players' move choices from as few as 20 games by enriching a population-level model with prototype players and then initializing personal embeddings via prototype matching.
PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning
cs.LG 2025-07 conditional novelty 6.0 of 10

PROL achieves state-of-the-art rehearsal-free online continual learning accuracy on CIFAR100, ImageNet-R, ImageNet-A, and CUB with a single lightweight prompt generator and 16 trainable numbers per class.

See all 212 Pith citations

Reference graph

Works this paper leans on

294 extracted references · 294 canonical work pages · cited by 212 Pith papers (see all)

[1]

Roy Bar-Haim, Ido Dagan, Bill Dolan, Lisa Ferro, Danilo Giampiccolo, Bernardo Magnini, and Idan Szpektor. 2006. The second PASCAL recognising textual entailment challenge. In Proceedings of the second PASCAL challenges workshop on recognising textual entailment, volume 6, pages 6--4. Venice

work page 2006
[2]

Luisa Bentivogli, Peter Clark, Ido Dagan, and Danilo Giampiccolo. 2009. The fifth PASCAL recognizing textual entailment challenge. In TAC

work page 2009
[3]

James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake Vander P las, Skye Wanderman- M ilne, and Qiao Zhang. 2018. http://github.com/google/jax JAX : composable transformations of P ython+ N um P y programs

work page 2018
[4]

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gr...

work page 2020
[5]

Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. 2019. https://doi.org/10.18653/v1/N19-1300 B ool Q : Exploring the surprising difficulty of natural yes/no questions . In Proceedings of the 2019 Conference of the North A merican Chapter of the Association for Computational Linguistics: Human Language...

work page doi:10.18653/v1/n19-1300 2019
[6]

Ido Dagan, Oren Glickman, and Bernardo Magnini. 2005. The PASCAL recognising textual entailment challenge. In Machine Learning Challenges Workshop, pages 177--190. Springer

work page 2005
[7]

Marie-Catherine De Marneff, Mandy Simons, and Judith Tonhauser. 2019. The CommitmentBank : Investigating projection in naturally occurring discourse. Proceedings of Sinn und Bedeutung 23

work page 2019
[8]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. https://doi.org/10.18653/v1/N19-1423 BERT : Pre-training of deep bidirectional transformers for language understanding . In Proceedings of the 2019 Conference of the North A merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long a...

work page doi:10.18653/v1/n19-1423 2019

Show all 294 references

[9]

William B Dolan and Chris Brockett. 2005. Automatically constructing a corpus of sentential paraphrases. In Proceedings of the Third International Workshop on Paraphrasing (IWP2005)

2005
[10]

Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. 2019. https://doi.org/10.18653/v1/N19-1246 DROP : A reading comprehension benchmark requiring discrete reasoning over paragraphs . In Proceedings of the 2019 Conference of the North A ...

2019 doi
[11]

Adam Fisch, Alon Talmor, Robin Jia, Minjoon Seo, Eunsol Choi, and Danqi Chen. 2019. MRQA 2019 shared task: Evaluating generalization in reading comprehension. In Proceedings of 2nd Machine Reading for Reading Comprehension (MRQA) Workshop at EMNLP

2019
[12]

Danilo Giampiccolo, Bernardo Magnini, Ido Dagan, and Bill Dolan. 2007. The third PASCAL recognizing textual entailment challenge. In Proceedings of the ACL-PASCAL workshop on textual entailment and paraphrasing, pages 1--9. Association for Computational Linguistics

2007
[14]

L. K. Hansen and P. Salamon . 1990. https://doi.org/10.1109/34.58871 Neural network ensembles . IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(10):993--1001

1990 doi
[15]

Jonathan Heek, Anselm Levskaya, Avital Oliver, Marvin Ritter, Bertrand Rondepierre, Andreas Steiner, and Marc van Z ee. 2020. http://github.com/google/flax F lax: A neural network library and ecosystem for JAX

2020
[16]

Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019. http://proceedings.mlr.press/v97/houlsby19a.html Parameter-efficient transfer learning for NLP . In Proceedings of the 36th Int...

2019
[17]

Jeremy Howard and Sebastian Ruder. 2018. https://doi.org/10.18653/v1/P18-1031 Universal language model fine-tuning for text classification . In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 328--339, Melb...

2018 doi
[18]

Shankar Iyer, Nikhil Dandekar, and Kornel Csernai. 2017. https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs First Q uora dataset release: Question pairs

2017
[19]

Xu, Jun Araki, and Graham Neubig

Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig. 2020. https://doi.org/10.1162/tacl_a_00324 How can we know what language models know? Transactions of the Association for Computational Linguistics, 8:423--438

2020 doi
[20]

Kembhavi , M

A. Kembhavi , M. Seo , D. Schwenk , J. Choi , A. Farhadi , and H. Hajishirzi . 2017. https://doi.org/10.1109/CVPR.2017.571 Are you smarter than a sixth grader? textbook question answering for multimodal machine comprehension . In 2017 IEEE Conference on Computer Vision and Pat...

2017 doi
[21]

Daniel Khashabi, Snigdha Chaturvedi, Michael Roth, Shyam Upadhyay, and Dan Roth. 2018. Looking beyond the surface: A challenge set for reading comprehension over multiple sentences. In Proceedings of North American Chapter of the Association for Computational Linguistics (NAACL)

2018
[22]

Vid Kocijan, Ana-Maria Cretu, Oana-Maria Camburu, Yordan Yordanov, and Thomas Lukasiewicz. 2019. https://doi.org/10.18653/v1/P19-1478 A surprisingly robust trick for the W inograd schema challenge . In Proceedings of the 57th Annual Meeting of the Association for Computational...

2019 doi
[24]

Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy. 2017. https://doi.org/10.18653/v1/D17-1082 RACE : Large-scale R e A ding comprehension dataset from examinations . In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages...

2017 doi
[25]

Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. 2017. https://proceedings.neurips.cc/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Simple and scalable predictive uncertainty estimation using deep ensembles . In Advances in Neural Information Proc...

2017
[26]

Hector Levesque, Ernest Davis, and Leora Morgenstern. 2012. The W inograd schema challenge. In Thirteenth International Conference on the Principles of Knowledge Representation and Reasoning

2012
[27]

Omer Levy, Minjoon Seo, Eunsol Choi, and Luke Zettlemoyer. 2017. https://doi.org/10.18653/v1/K17-1034 Zero-shot relation extraction via reading comprehension . In Proceedings of the 21st Conference on Computational Natural Language Learning ( C o NLL 2017) , pages 333--342, Va...

2017 doi
[28]

Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020. https://doi.org/10.18653/v1/2020.acl-main.703 BART : Denoising sequence-to-sequence pre-training for natural language generation, translatio...

2020 doi
[30]

Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang. 2021. http://arxiv.org/abs/2103.10385 GPT understands, too . CoRR, abs/2103.10385

2021
[31]

Lajanugen Logeswaran, Ann Lee, Myle Ott, Honglak Lee, Marc'Aurelio Ranzato, and Arthur Szlam. 2020. http://arxiv.org/abs/2012.09543 Few-shot sequence learning with transformers . CoRR, abs/2012.09543

2020
[32]

Vinod Nair and Geoffrey E. Hinton. 2010. Rectified linear units improve restricted B oltzmann machines. In Proceedings of the 27th International Conference on International Conference on Machine Learning, ICML'10, page 807–814, Madison, WI, USA. Omnipress

2010
[33]

Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. https://doi.org/10.18653/v1/N18-1202 Deep contextualized word representations . In Proceedings of the 2018 Conference of the North A merican Chapter of the Assoc...

2018 doi
[34]

Jonas Pfeiffer, Ivan Vuli \'c , Iryna Gurevych, and Sebastian Ruder. 2020. https://doi.org/10.18653/v1/2020.emnlp-main.617 MAD-X : A n A dapter- B ased F ramework for M ulti- T ask C ross- L ingual T ransfer . In Proceedings of the 2020 Conference on Empirical Methods in Natur...

2020 doi
[35]

Mohammad Taher Pilehvar and Jose Camacho - Collados. 2018. http://arxiv.org/abs/1808.09121 WiC : 10,000 example pairs for evaluating context-sensitive representations . CoRR, abs/1808.09121

2018
[37]

Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018. https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf Improving language understanding by generative pre-training

2018
[38]

Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf Language models are unsupervised multitask learners . OpenAI Blog

2019
[39]

Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. http://jmlr.org/papers/v21/20-074.html Exploring the limits of transfer learning with a unified text-to-text transformer . Journal of Machine Lea...

2020
[41]

Sylvestre-Alvise Rebuffi, Hakan Bilen, and Andrea Vedaldi. 2017. https://proceedings.neurips.cc/paper/2017/file/e7b24b112a44fdd9ee93bdf998c6ca0e-Paper.pdf Learning multiple visual domains with residual adapters . In Advances in Neural Information Processing Systems, volume 30....

2017
[42]

Melissa Roemmele, Cosmin Adrian Bejan, and Andrew S Gordon. 2011. Choice of plausible alternatives: An evaluation of commonsense causal reasoning. In 2011 AAAI Spring Symposium Series

2011
[43]

Khapra, and Karthik Sankaranarayanan

Amrita Saha, Rahul Aralikatte, Mitesh M. Khapra, and Karthik Sankaranarayanan. 2018. https://doi.org/10.18653/v1/P18-1156 D uo RC : Towards complex language understanding with paraphrased reading comprehension . In Proceedings of the 56th Annual Meeting of the Association for ...

2018 doi
[44]

Timo Schick and Hinrich Sch \"u tze. 2021. https://aclanthology.org/2021.eacl-main.20 Exploiting cloze-questions for few-shot text classification and natural language inference . In Proceedings of the 16th Conference of the European Chapter of the Association for Computational...

2021
[45]

Noam Shazeer. 2020. http://arxiv.org/abs/2002.05202 GLU variants improve transformer . CoRR, abs/2002.05202

2020 arXiv
[46]

Noam Shazeer and Mitchell Stern. 2018. http://proceedings.mlr.press/v80/shazeer18a.html Adafactor: Adaptive learning rates with sublinear memory cost . In Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Researc...

2018
[47]

Logan IV, Eric Wallace, and Sameer Singh

Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. 2020. https://doi.org/10.18653/v1/2020.emnlp-main.346 A uto P rompt: E liciting K nowledge from L anguage M odels with A utomatically G enerated P rompts . In Proceedings of the 2020 Conference o...

2020 doi
[48]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, ukasz Kaiser, and Illia Polosukhin. 2017. https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf Attention is all you need . In Advances in Neural Informa...

2017
[49]

Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2019 a . https://proceedings.neurips.cc/paper/2019/file/4496bf24afe7fab6f046bf4923da8de6-Paper.pdf SuperGLUE : A stickier benchmark for general-purpose lang...

2019
[50]

Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2019 b . GLUE : A multi-task benchmark and analysis platform for natural language understanding. In the Proceedings of ICLR

2019
[52]

Sheng Zhang, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Kevin Duh, and Benjamin Van Durme. 2018. http://arxiv.org/abs/1810.12885 ReCoRD : Bridging the gap between human and machine commonsense reading comprehension . CoRR, abs/1810.12885

2018
[53]

Language Models are Few-Shot Learners , url =

Brown, Tom and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared D and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and Agarwal, Sandhini and Herbert-Voss, Ariel and Krueger, Gretchen and Henighan, Tom a...
[54]

Liu , title =

Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu , title =. Journal of Machine Learning Research , year =
[55]

Prefix-Tuning: Optimizing Continuous Prompts for Generation

Li, Xiang Lisa and Liang, Percy. Prefix-Tuning: Optimizing Continuous Prompts for Generation. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Pape...

2021 doi
[56]

WARP : W ord-level A dversarial R e P rogramming

Hambardzumyan, Karen and Khachatrian, Hrant and May, Jonathan. WARP : W ord-level A dversarial R e P rogramming. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (...

2021 doi
[57]

CoRR , volume =

Lajanugen Logeswaran and Ann Lee and Myle Ott and Honglak Lee and Marc'Aurelio Ranzato and Arthur Szlam , title =. CoRR , volume =. 2020 , url =

2020
[58]

Iyer, Shankar and Dandekar, Nikhil and Csernai, Kornel , title =
[59]

Proceedings of the Third International Workshop on Paraphrasing (IWP2005) , year=

Automatically constructing a corpus of sentential paraphrases , author=. Proceedings of the Third International Workshop on Paraphrasing (IWP2005) , year=
[60]

Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R. , note=
[61]

Adam Fisch and Alon Talmor and Robin Jia and Minjoon Seo and Eunsol Choi and Danqi Chen , booktitle=
[62]

CoRR , volume =

Armen Aghajanyan and Luke Zettlemoyer and Sonal Gupta , title =. CoRR , volume =. 2020 , url =

2020
[63]

Proceedings of the National Academy of Sciences , volume=

Transforming task representations to perform novel tasks , author=. Proceedings of the National Academy of Sciences , volume=. 2020 , publisher=

2020
[64]

Attention is All you Need , url =

Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser,. Attention is All you Need , url =. Advances in Neural Information Processing Systems , editor =
[65]

Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel , booktitle =
[66]

Proceedings of North American Chapter of the Association for Computational Linguistics (NAACL) , year =

Daniel Khashabi and Snigdha Chaturvedi and Michael Roth and Shyam Upadhyay and Dan Roth , title =. Proceedings of North American Chapter of the Association for Computational Linguistics (NAACL) , year =
[67]

Proceedings of Sinn und Bedeutung 23 , author=

The. Proceedings of Sinn und Bedeutung 23 , author=
[68]

Dagan, Ido and Glickman, Oren and Magnini, Bernardo , booktitle=. The. 2005 , organization=

2005
[69]

The second

Bar-Haim, Roy and Dagan, Ido and Dolan, Bill and Ferro, Lisa and Giampiccolo, Danilo and Magnini, Bernardo and Szpektor, Idan , booktitle=. The second. 2006 , organization=

2006
[70]

The third

Giampiccolo, Danilo and Magnini, Bernardo and Dagan, Ido and Dolan, Bill , booktitle=. The third. 2007 , organization=

2007
[71]

The Fifth

Bentivogli, Luisa and Clark, Peter and Dagan, Ido and Giampiccolo, Danilo , booktitle=. The Fifth
[72]

2011 AAAI Spring Symposium Series , year=

Choice of plausible alternatives: An evaluation of commonsense causal reasoning , author=. 2011 AAAI Spring Symposium Series , year=

2011
[73]

Levesque, Hector and Davis, Ernest and Morgenstern, Leora , booktitle=. The
[74]

CoRR , volume=

Mohammad Taher Pilehvar and Jose Camacho. CoRR , volume=. 2018 , url=

2018
[75]

Bowman , title =

Alex Warstadt and Amanpreet Singh and Samuel R. Bowman , title =. CoRR , volume =. 2018 , url =

2018
[76]

Proceedings of the 2013 conference on empirical methods in natural language processing , pages=

Recursive deep models for semantic compositionality over a sentiment treebank , author=. Proceedings of the 2013 conference on empirical methods in natural language processing , pages=

2013
[77]

arXiv preprint arXiv:1708.00055 , year=

Semeval-2017 task 1: Semantic textual similarity-multilingual and cross-lingual focused evaluation , author=. arXiv preprint arXiv:1708.00055 , year=

2017
[78]

A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference

Williams, Adina and Nangia, Nikita and Bowman, Samuel. A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies...

2018
[79]

SQ u AD : 100,000+ Questions for Machine Comprehension of Text

Rajpurkar, Pranav and Zhang, Jian and Lopyrev, Konstantin and Liang, Percy. SQ u AD : 100,000+ Questions for Machine Comprehension of Text. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 2016. doi:10.18653/v1/D16-1264

2016 doi
[80]

CoRR , volume =

Xiao Liu and Yanan Zheng and Zhengxiao Du and Ming Ding and Yujie Qian and Zhilin Yang and Jie Tang , title =. CoRR , volume =. 2021 , url =

2021
[81]

Language Models are Unsupervised Multitask Learners , author=
[82]

2013 IEEE International Conference on Acoustics, Speech and Signal Processing , title=

A. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing , title=. 2013 , volume=

2013
[83]

S entence P iece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing

Kudo, Taku and Richardson, John. S entence P iece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. 2018. doi:10.18653/...

2018 doi
[84]

CoRR , volume =

Sheng Zhang and Xiaodong Liu and Jingjing Liu and Jianfeng Gao and Kevin Duh and Benjamin Van Durme , title =. CoRR , volume =. 2018 , url =

2018
[85]

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , title=

A. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , title=. 2017 , volume=

2017
[86]

James Bradbury and Roy Frostig and Peter Hawkins and Matthew James Johnson and Chris Leary and Dougal Maclaurin and George Necula and Adam Paszke and Jake Vander
[87]

Proceedings of the 35th International Conference on Machine Learning , pages =

Adafactor: Adaptive Learning Rates with Sublinear Memory Cost , author =. Proceedings of the 35th International Conference on Machine Learning , pages =. 2018 , editor =

2018
[88]

CoRR , volume =

Noam Shazeer , title =. CoRR , volume =. 2020 , url =

2020
[89]

, title =

Nair, Vinod and Hinton, Geoffrey E. , title =. Proceedings of the 27th International Conference on International Conference on Machine Learning , pages =. 2010 , isbn =

2010
[90]

Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference

Schick, Timo and Sch. Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. 2021

2021
[91]

IEEE Transactions on Pattern Analysis and Machine Intelligence , year=

Neural network ensembles , author=. IEEE Transactions on Pattern Analysis and Machine Intelligence , year=
[92]

Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles , url =

Lakshminarayanan, Balaji and Pritzel, Alexander and Blundell, Charles , booktitle =. Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles , url =
[93]

Achille and M

A. Achille and M. Lam and R. Tewari and A. Ravichandran and S. Maji and C. Fowlkes and S. Soatto and P. Perona , booktitle =. 2019 , volume =. doi:10.1109/ICCV.2019.00653 , url =

2019 doi
[94]

Parameter-Efficient Transfer Learning for

Houlsby, Neil and Giurgiu, Andrei and Jastrzebski, Stanislaw and Morrone, Bruna and De Laroussilhe, Quentin and Gesmundo, Andrea and Attariyan, Mona and Gelly, Sylvain , booktitle =. Parameter-Efficient Transfer Learning for. 2019 , editor =

2019
[95]

Learning multiple visual domains with residual adapters , url =

Rebuffi, Sylvestre-Alvise and Bilen, Hakan and Vedaldi, Andrea , booktitle =. Learning multiple visual domains with residual adapters , url =
[96]

Learning How to Ask: Querying LM s with Mixtures of Soft Prompts

Qin, Guanghui and Eisner, Jason. Learning How to Ask: Querying LM s with Mixtures of Soft Prompts. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021. doi:10.18653/v1/2021.naacl-main.410

2021 doi
[97]

2018 , url =

Improving Language Understanding by Generative Pre-Training , author =. 2018 , url =

2018
[98]

Waskom , title =

Michael L. Waskom , title =. 2021 , publisher =. doi:10.21105/joss.03021 , url =

2021 doi
[99]

Jonathan Heek and Anselm Levskaya and Avital Oliver and Marvin Ritter and Bertrand Rondepierre and Andreas Steiner and Marc van
[100]

Proceedings of the 11th Global Wordnet Conference. 2021

2021
[101]

On Universal Colexifications

Bao, Hongchang and Hauer, Bradley and Kondrak, Grzegorz. On Universal Colexifications. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[102]

UZWORDNET : A Lexical-Semantic Database for the U zbek Language

Agostini, Alessandro and Usmanov, Timur and Khamdamov, Ulugbek and Abdurakhmonova, Nilufar and Mamasaidov, Mukhammadsaid. UZWORDNET : A Lexical-Semantic Database for the U zbek Language. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[103]

Practical Approach on Implementation of W ord N ets for S outh A frican Languages

Sefara, Tshephisho Joseph and Mokgonyane, Tumisho Billson and Marivate, Vukosi. Practical Approach on Implementation of W ord N ets for S outh A frican Languages. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[104]

Homonymy and Polysemy Detection with Multilingual Information

Habibi, Amir Ahmad and Hauer, Bradley and Kondrak, Grzegorz. Homonymy and Polysemy Detection with Multilingual Information. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[105]

Taboo W ordnet

Bond, Francis and Choo, Merrick Yeu Herng. Taboo W ordnet. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[106]

A sk2 T ransformers: Zero-Shot Domain labelling with Pretrained Language Models

Sainz, Oscar and Rigau, German. A sk2 T ransformers: Zero-Shot Domain labelling with Pretrained Language Models. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[107]

Discriminating Homonymy from Polysemy in Wordnets: E nglish, S panish and P olish Nouns

Janz, Arkadiusz and Maziarz, Marek. Discriminating Homonymy from Polysemy in Wordnets: E nglish, S panish and P olish Nouns. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[108]

Implementing ASLN et V1.0: Progress and Plans

Lualdi, Colin and Wright, Elaine and Hudson, Jack and Caselli, Naomi and Fellbaum, Christiane. Implementing ASLN et V1.0: Progress and Plans. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[109]

Monolingual Word Sense Alignment as a Classification Problem

Ahmadi, Sina and McCrae, John P. Monolingual Word Sense Alignment as a Classification Problem. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[110]

Extraction of Common-Sense Relations from Procedural Task Instructions using BERT

Losing, Viktor and Fischer, Lydia and Deigm. Extraction of Common-Sense Relations from Procedural Task Instructions using BERT. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[111]

and Goodman, Michael Wayne and Bond, Francis and Rademaker, Alexandre and Rudnicka, Ewa and Costa, Luis Morgado Da

McCrae, John P. and Goodman, Michael Wayne and Bond, Francis and Rademaker, Alexandre and Rudnicka, Ewa and Costa, Luis Morgado Da. The G lobal W ord N et Formats: Updates for 2020. Proceedings of the 11th Global Wordnet Conference. 2021

2020
[112]

Intrinsically Interlingual: The Wn Python Library for Wordnets

Goodman, Michael Wayne and Bond, Francis. Intrinsically Interlingual: The Wn Python Library for Wordnets. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[113]

Semantic Analysis of Verb-Noun Derivation in P rinceton W ord N et

Mititelu, Verginica and Leseva, Svetlozara and Stoyanova, Ivelina. Semantic Analysis of Verb-Noun Derivation in P rinceton W ord N et. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[114]

Building the T urkish F rame N et

Mar. Building the T urkish F rame N et. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[115]

Evaluation of Taxonomy Enrichment on Diachronic W ord N et Versions

Nikishina, Irina and Loukachevitch, Natalia and Logacheva, Varvara and Panchenko, Alexander. Evaluation of Taxonomy Enrichment on Diachronic W ord N et Versions. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[116]

A (Non)-Perfect Match: Mapping pl W ord N et onto P rinceton W ord N et

Rudnicka, Ewa and Witkowski, Wojciech and Piasecki, Maciej. A (Non)-Perfect Match: Mapping pl W ord N et onto P rinceton W ord N et. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[117]

P ersian S em C or: A Bag of Word Sense Annotated Corpus for the P ersian Language

Rouhizadeh, Hossein and Shamsfard, Mehrnoush and Dehghan, Mahdi and Rouhizadeh, Masoud. P ersian S em C or: A Bag of Word Sense Annotated Corpus for the P ersian Language. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[118]

Proceedings of the 11th Global Wordnet Conference

H is N et: A Polarity Lexicon based on W ord N et for Emotion Analysis. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[119]

T urkish W ord N et K e N et

Bakay,. T urkish W ord N et K e N et. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[120]

Enriching pl W ord N et with morphology

Dziob, Agnieszka and Walentynowicz, Wiktor. Enriching pl W ord N et with morphology. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[121]

Towards Expanding W ord N et with Conceptual Frames

Svetla, Koeva. Towards Expanding W ord N et with Conceptual Frames. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[122]

O de N et: Compiling a G erman W ord N et from other Resources

Siegel, Melanie and Bond, Francis. O de N et: Compiling a G erman W ord N et from other Resources. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[123]

Comparing Similarity of Words Based on Psychosemantic Experiment and R u W ord N et

Solovyev, Valery and Loukachevitch, Natalia. Comparing Similarity of Words Based on Psychosemantic Experiment and R u W ord N et. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[124]

Text Document Clustering: W ordnet vs

Marci \'n czuk, Micha and Gniewkowski, Mateusz and Walkowiak, Tomasz and B e dkowski, Marcin. Text Document Clustering: W ordnet vs. TF - IDF vs. Word Embeddings. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[125]

Extracting Synonyms from Bilingual Dictionaries

Jarrar, Mustafa and Naser, Eman and Khalifa, Muhammad and Shaalan, Khaled. Extracting Synonyms from Bilingual Dictionaries. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[126]

Neural Language Models vs W ordnet-based Semantically Enriched Representation in CST Relation Recognition

Janz, Arkadiusz and Piasecki, Maciej and W a torski, Piotr. Neural Language Models vs W ordnet-based Semantically Enriched Representation in CST Relation Recognition. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[127]

What is on Social Media that is not in W ord N et? A Preliminary Analysis on the T witter AAE Corpus

Domingo, Cecilia and Gonzalez-Ferrero, Tatiana and Gonzalez-Dios, Itziar. What is on Social Media that is not in W ord N et? A Preliminary Analysis on the T witter AAE Corpus. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[128]

Creating Domain Dependent T urkish W ord N et and S enti N et

Ar. Creating Domain Dependent T urkish W ord N et and S enti N et. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[129]

and Cillessen, David

McCrae, John P. and Cillessen, David. Towards a Linking between W ord N et and W ikidata. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[130]

Toward the creation of W ord N ets for ancient I ndo- E uropean languages

Biagetti, Erica and Zanchi, Chiara and Short, William Michael. Toward the creation of W ord N ets for ancient I ndo- E uropean languages. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[131]

D an N et2: Extending the coverage of adjectives in D an N et based on thesaurus data (project presentation)

Nimb, Sanni and Pedersen, Bolette and Olsen, Sussi. D an N et2: Extending the coverage of adjectives in D an N et based on thesaurus data (project presentation). Proceedings of the 11th Global Wordnet Conference. 2021

2021
[132]

Teaching Through Tagging --- Interactive Lexical Semantics

Bond, Francis and Devadason, Andrew and Teo, Melissa Rui Lin and da Costa, Lu \' s Morgado. Teaching Through Tagging --- Interactive Lexical Semantics. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[133]

Towards the Addition of Pronunciation Information to Lexical Semantic Resources

Declerck, Thierry and Baj c eti \'c , Lenka. Towards the Addition of Pronunciation Information to Lexical Semantic Resources. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[134]

Testing agreement between lexicographers: A case of homonymy and polysemy

Maziarz, Marek and Bond, Francis and Rudnicka, Ewa. Testing agreement between lexicographers: A case of homonymy and polysemy. Proceedings of the 11th Global Wordnet Conference. 2021

2021
[135]

Proceedings of the 8th International Workshop on Mining Scientific Publications. 2020

2020
[136]

Virtual Citation Proximity ( VCP ): Empowering Document Recommender Systems by Learning a Hypothetical In-Text Citation-Proximity Metric for Uncited Documents

Molloy, Paul and Beel, Joeran and Aizawa, Akiko. Virtual Citation Proximity ( VCP ): Empowering Document Recommender Systems by Learning a Hypothetical In-Text Citation-Proximity Metric for Uncited Documents. Proceedings of the 8th International Workshop on Mining Scientific P...

2020
[137]

Citations Beyond Self Citations: Identifying Authors, Affiliations, and Nationalities in Scientific Papers

Matsubara, Yoshitomo and Singh, Sameer. Citations Beyond Self Citations: Identifying Authors, Affiliations, and Nationalities in Scientific Papers. Proceedings of the 8th International Workshop on Mining Scientific Publications. 2020

2020
[138]

S mart C ite C on: Implicit Citation Context Extraction from Academic Literature Using Supervised Learning

Guo, Chenrui and Cui, Haoran and Zhang, Li and Wang, Jiamin and Lu, Wei and Wu, Jian. S mart C ite C on: Implicit Citation Context Extraction from Academic Literature Using Supervised Learning. Proceedings of the 8th International Workshop on Mining Scientific Publications. 2020

2020
[139]

Synthetic vs

Grennan, Mark and Beel, Joeran. Synthetic vs. Real Reference Strings for Citation Parsing, and the Importance of Re-training and Out-Of-Sample Data for Meaningful Evaluations: Experiments with GROBID , GIANT and CORA. Proceedings of the 8th International Workshop on Mining Sci...

2020
[140]

Term-Recency for TF - IDF , BM 25 and USE Term Weighting

Marwah, Divyanshu and Beel, Joeran. Term-Recency for TF - IDF , BM 25 and USE Term Weighting. Proceedings of the 8th International Workshop on Mining Scientific Publications. 2020

2020
[141]

The Normalized Impact Index for Keywords in Scholarly Papers to Detect Subtle Research Topics

Ikeda, Daisuke and Taniguchi, Yuta and Koga, Kazunori. The Normalized Impact Index for Keywords in Scholarly Papers to Detect Subtle Research Topics. Proceedings of the 8th International Workshop on Mining Scientific Publications. 2020

2020
[142]

Representing and Reconstructing P hy SH : Which Embedding Competent?

Chen, Xiaoli and Zhang, Zhixiong. Representing and Reconstructing P hy SH : Which Embedding Competent?. Proceedings of the 8th International Workshop on Mining Scientific Publications. 2020

2020
[143]

Combining Representations For Effective Citation Classification

de Andrade, Claudio Mois \'e s Valiense and Gon c alves, Marcos Andr \'e. Combining Representations For Effective Citation Classification. Proceedings of the 8th International Workshop on Mining Scientific Publications. 2020

2020
[144]

Scubed at 3 C task A - A simple baseline for citation context purpose classification

Mishra, Shubhanshu and Mishra, Sudhanshu. Scubed at 3 C task A - A simple baseline for citation context purpose classification. Proceedings of the 8th International Workshop on Mining Scientific Publications. 2020

2020
[145]

Scubed at 3 C task B - A simple baseline for citation context influence classification

Mishra, Shubhanshu and Mishra, Sudhanshu. Scubed at 3 C task B - A simple baseline for citation context influence classification. Proceedings of the 8th International Workshop on Mining Scientific Publications. 2020

2020
[146]

A mrita \_ CEN \_ NLP @ WOSP 3 C Citation Context Classification Task

B, Premjith and KP, Soman. A mrita \_ CEN \_ NLP @ WOSP 3 C Citation Context Classification Task. Proceedings of the 8th International Workshop on Mining Scientific Publications. 2020

2020
[147]

Overview of the 2020 WOSP 3 C Citation Context Classification Task

Kunnath, Suchetha Nambanoor and Pride, David and Gyawali, Bikash and Knoth, Petr. Overview of the 2020 WOSP 3 C Citation Context Classification Task. Proceedings of the 8th International Workshop on Mining Scientific Publications. 2020

2020
[148]

Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020

2020
[149]

May I Ask Who ' s Calling? Named Entity Recognition on Call Center Transcripts for Privacy Law Compliance

Kaplan, Micaela. May I Ask Who ' s Calling? Named Entity Recognition on Call Center Transcripts for Privacy Law Compliance. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.1

2020 doi
[150]

`` Did you really mean what you said? '' : Sarcasm Detection in H indi- E nglish Code-Mixed Data using Bilingual Word Embeddings

Aggarwal, Akshita and Wadhawan, Anshul and Chaudhary, Anshima and Maurya, Kavita. `` Did you really mean what you said? '' : Sarcasm Detection in H indi- E nglish Code-Mixed Data using Bilingual Word Embeddings. Proceedings of the Sixth Workshop on Noisy User-generated Text (W...

2020 doi
[151]

Noisy Text Data: Achilles ' Heel of BERT

Kumar, Ankit and Makhija, Piyush and Gupta, Anuj. Noisy Text Data: Achilles ' Heel of BERT. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.3

2020 doi
[152]

Determining Question-Answer Plausibility in Crowdsourced Datasets Using Multi-Task Learning

Gardner, Rachel and Varma, Maya and Zhu, Clare and Krishna, Ranjay. Determining Question-Answer Plausibility in Crowdsourced Datasets Using Multi-Task Learning. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.4

2020 doi
[153]

Combining BERT with Static Word Embeddings for Categorizing Social Media

Alghanmi, Israa and Espinosa Anke, Luis and Schockaert, Steven. Combining BERT with Static Word Embeddings for Categorizing Social Media. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.5

2020 doi
[154]

Enhanced Sentence Alignment Network for Efficient Short Text Matching

Hu, Zhe and Fu, Zuohui and Peng, Cheng and Wang, Weiwei. Enhanced Sentence Alignment Network for Efficient Short Text Matching. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.6

2020 doi
[155]

PHINC : A Parallel H inglish Social Media Code-Mixed Corpus for Machine Translation

Srivastava, Vivek and Singh, Mayank. PHINC : A Parallel H inglish Social Media Code-Mixed Corpus for Machine Translation. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.7

2020 doi
[156]

Cross-lingual sentiment classification in low-resource B engali language

Sazzed, Salim. Cross-lingual sentiment classification in low-resource B engali language. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.8

2020 doi
[157]

The Non-native Speaker Aspect: I ndian E nglish in Social Media

Sarkar, Rupak and Mahinder, Sayantan and KhudaBukhsh, Ashiqur. The Non-native Speaker Aspect: I ndian E nglish in Social Media. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.9

2020 doi
[158]

Sentence Boundary Detection on Line Breaks in J apanese

Hayashibe, Yuta and Mitsuzawa, Kensuke. Sentence Boundary Detection on Line Breaks in J apanese. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.10

2020 doi
[159]

Non-ingredient Detection in User-generated Recipes using the Sequence Tagging Approach

Yamaguchi, Yasuhiro and Inuzuka, Shintaro and Hiramatsu, Makoto and Harashima, Jun. Non-ingredient Detection in User-generated Recipes using the Sequence Tagging Approach. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.11

2020 doi
[160]

Generating Fact Checking Summaries for Web Claims

Mishra, Rahul and Gupta, Dhruv and Leippold, Markus. Generating Fact Checking Summaries for Web Claims. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.12

2020 doi
[161]

Intelligent Analyses on Storytelling for Impact Measurement

Kicken, Koen and De Maesschalck, Tessa and Vanrumste, Bart and De Keyser, Tom and Shim, Hee Reen. Intelligent Analyses on Storytelling for Impact Measurement. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.13

2020 doi
[162]

An Empirical Analysis of Human-Bot Interaction on R eddit

Ma, Ming-Cheng and Lalor, John P. An Empirical Analysis of Human-Bot Interaction on R eddit. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.14

2020 doi
[163]

Detecting Trending Terms in Cybersecurity Forum Discussions

Hughes, Jack and Aycock, Seth and Caines, Andrew and Buttery, Paula and Hutchings, Alice. Detecting Trending Terms in Cybersecurity Forum Discussions. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.15

2020 doi
[164]

Service registration chatbot: collecting and comparing dialogues from AMT workers and service ' s users

Molteni, Luca and Singh, Mittul and Leinonen, Juho and Leino, Katri and Kurimo, Mikko and Della Valle, Emanuele. Service registration chatbot: collecting and comparing dialogues from AMT workers and service ' s users. Proceedings of the Sixth Workshop on Noisy User-generated T...

2020 doi
[165]

Automated Assessment of Noisy Crowdsourced Free-text Answers for H indi in Low Resource Setting

Agarwal, Dolly and Gupta, Somya and Baghel, Nishant. Automated Assessment of Noisy Crowdsourced Free-text Answers for H indi in Low Resource Setting. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.17

2020 doi
[166]

Punctuation Restoration using Transformer Models for High-and Low-Resource Languages

Alam, Tanvirul and Khan, Akib and Alam, Firoj. Punctuation Restoration using Transformer Models for High-and Low-Resource Languages. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.18

2020 doi
[167]

Truecasing G erman user-generated conversational text

Grishina, Yulia and Gueudre, Thomas and Winkler, Ralf. Truecasing G erman user-generated conversational text. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.19

2020 doi
[168]

Fine-Tuning MT systems for Robustness to Second-Language Speaker Variations

Alam, Md Mahfuz Ibn and Anastasopoulos, Antonios. Fine-Tuning MT systems for Robustness to Second-Language Speaker Variations. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.20

2020 doi
[169]

Impact of ASR on A lzheimer ' s Disease Detection: All Errors are Equal, but Deletions are More Equal than Others

Balagopalan, Aparna and Shkaruta, Ksenia and Novikova, Jekaterina. Impact of ASR on A lzheimer ' s Disease Detection: All Errors are Equal, but Deletions are More Equal than Others. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653...

2020 doi
[170]

Detecting Entailment in Code-Mixed H indi- E nglish Conversations

Chakravarthy, Sharanya and Umapathy, Anjana and Black, Alan W. Detecting Entailment in Code-Mixed H indi- E nglish Conversations. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.22

2020 doi
[171]

Detecting Objectifying Language in Online Professor Reviews

Waller, Angie and Gorman, Kyle. Detecting Objectifying Language in Online Professor Reviews. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.23

2020 doi
[172]

Annotation Efficient Language Identification from Weak Labels

Palakodety, Shriphani and KhudaBukhsh, Ashiqur. Annotation Efficient Language Identification from Weak Labels. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.24

2020 doi
[173]

Fantastic Features and Where to Find Them: Detecting Cognitive Impairment with a Subsequence Classification Guided Approach

Eyre, Ben and Balagopalan, Aparna and Novikova, Jekaterina. Fantastic Features and Where to Find Them: Detecting Cognitive Impairment with a Subsequence Classification Guided Approach. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18...

2020 doi
[174]

Quantifying the Evaluation of Heuristic Methods for Textual Data Augmentation

Kashefi, Omid and Hwa, Rebecca. Quantifying the Evaluation of Heuristic Methods for Textual Data Augmentation. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.26

2020 doi
[175]

An Empirical Survey of Unsupervised Text Representation Methods on T witter Data

Wang, Lili and Gao, Chongyang and Wei, Jason and Ma, Weicheng and Liu, Ruibo and Vosoughi, Soroush. An Empirical Survey of Unsupervised Text Representation Methods on T witter Data. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653...

2020 doi
[176]

and Dredze, Mark

Sech, Justin and DeLucia, Alexandra and Buczak, Anna L. and Dredze, Mark. Civil Unrest on T witter ( CUT ): A Dataset of Tweets to Support Research on Civil Unrest. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.28

2020 doi
[177]

Tweeki: Linking Named Entities on T witter to a Knowledge Graph

Harandizadeh, Bahareh and Singh, Sameer. Tweeki: Linking Named Entities on T witter to a Knowledge Graph. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.29

2020 doi
[178]

Representation learning of writing style

Hay, Julien and Doan, Bich-Lien and Popineau, Fabrice and Ait Elhara, Ouassim. Representation learning of writing style. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.30

2020 doi
[179]

`` A Little Birdie Told Me

Radhakrishnan, Karthik and Kanakagiri, Tushar and Chakravarthy, Sharanya and Balachandran, Vidhisha. `` A Little Birdie Told Me ... '' - Inductive Biases for Rumour Stance Detection on Social Media. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2...

2020 doi
[180]

Paraphrase Generation via Adversarial Penalizations

Vizcarra, Gerson and Ochoa-Luna, Jose. Paraphrase Generation via Adversarial Penalizations. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.32

2020 doi
[181]

WNUT -2020 Task 1 Overview: Extracting Entities and Relations from Wet Lab Protocols

Tabassum, Jeniya and Xu, Wei and Ritter, Alan. WNUT -2020 Task 1 Overview: Extracting Entities and Relations from Wet Lab Protocols. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.33

2020 doi
[182]

IITKGP at W - NUT 2020 Shared Task-1: Domain specific BERT representation for Named Entity Recognition of lab protocol

Vaidhya, Tejas and Kaushal, Ayush. IITKGP at W - NUT 2020 Shared Task-1: Domain specific BERT representation for Named Entity Recognition of lab protocol. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.34

2020 doi
[183]

P ublish I n C ovid19 at WNUT 2020 Shared Task-1: Entity Recognition in Wet Lab Protocols using Structured Learning Ensemble and Contextualised Embeddings

Singh, Janvijay and Wadhawan, Anshul. P ublish I n C ovid19 at WNUT 2020 Shared Task-1: Entity Recognition in Wet Lab Protocols using Structured Learning Ensemble and Contextualised Embeddings. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. ...

2020 doi
[184]

Big Green at WNUT 2020 Shared Task-1: Relation Extraction as Contextualized Sequence Classification

Miller, Chris and Vosoughi, Soroush. Big Green at WNUT 2020 Shared Task-1: Relation Extraction as Contextualized Sequence Classification. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.36

2020 doi
[185]

WNUT 2020 Shared Task-1: Conditional Random Field( CRF ) based Named Entity Recognition( NER ) for Wet Lab Protocols

Acharya, Kaushik. WNUT 2020 Shared Task-1: Conditional Random Field( CRF ) based Named Entity Recognition( NER ) for Wet Lab Protocols. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.37

2020 doi
[186]

mgsohrab at WNUT 2020 Shared Task-1: Neural Exhaustive Approach for Entity and Relation Recognition Over Wet Lab Protocols

Sohrab, Mohammad Golam and Duong Nguyen, Anh-Khoa and Miwa, Makoto and Takamura, Hiroya. mgsohrab at WNUT 2020 Shared Task-1: Neural Exhaustive Approach for Entity and Relation Recognition Over Wet Lab Protocols. Proceedings of the Sixth Workshop on Noisy User-generated Text (...

2020 doi
[187]

Fancy Man Launches Zippo at WNUT 2020 Shared Task-1: A Bert Case Model for Wet Lab Entity Extraction

Zeng, Qingcheng and Fang, Xiaoyang and Liang, Zhexin and Meng, Haoding. Fancy Man Launches Zippo at WNUT 2020 Shared Task-1: A Bert Case Model for Wet Lab Entity Extraction. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020...

2020 doi
[188]

B i T e M at WNUT 2020 Shared Task-1: Named Entity Recognition over Wet Lab Protocols using an Ensemble of Contextual Language Models

Knafou, Julien and Naderi, Nona and Copara, Jenny and Teodoro, Douglas and Ruch, Patrick. B i T e M at WNUT 2020 Shared Task-1: Named Entity Recognition over Wet Lab Protocols using an Ensemble of Contextual Language Models. Proceedings of the Sixth Workshop on Noisy User-gene...

2020 doi
[189]

WNUT -2020 Task 2: Identification of Informative COVID -19 E nglish Tweets

Nguyen, Dat Quoc and Vu, Thanh and Rahimi, Afshin and Dao, Mai Hoang and Nguyen, Linh The and Doan, Long. WNUT -2020 Task 2: Identification of Informative COVID -19 E nglish Tweets. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653...

2020 doi
[190]

TATL at WNUT -2020 Task 2: A Transformer-based Baseline System for Identification of Informative COVID -19 E nglish Tweets

Tuan Nguyen, Anh. TATL at WNUT -2020 Task 2: A Transformer-based Baseline System for Identification of Informative COVID -19 E nglish Tweets. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.42

2020 doi
[191]

NHK \_ STRL at WNUT -2020 Task 2: GAT s with Syntactic Dependencies as Edges and CTC -based Loss for Text Classification

Yasuda, Yuki and Ishiwatari, Taichi and Miyazaki, Taro and Goto, Jun. NHK \_ STRL at WNUT -2020 Task 2: GAT s with Syntactic Dependencies as Edges and CTC -based Loss for Text Classification. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. do...

2020 doi
[192]

NLP North at WNUT -2020 Task 2: Pre-training versus Ensembling for Detection of Informative COVID -19 E nglish Tweets

Giovanni M ller, Anders and van der Goot, Rob and Plank, Barbara. NLP North at WNUT -2020 Task 2: Pre-training versus Ensembling for Detection of Informative COVID -19 E nglish Tweets. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18...

2020 doi
[193]

Siva at WNUT -2020 Task 2: Fine-tuning Transformer Neural Networks for Identification of Informative Covid-19 Tweets

Sai, Siva. Siva at WNUT -2020 Task 2: Fine-tuning Transformer Neural Networks for Identification of Informative Covid-19 Tweets. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.45

2020 doi
[194]

IIITBH at WNUT -2020 Task 2: Exploiting the best of both worlds

Reddy, Saichethan and Biswal, Pradeep. IIITBH at WNUT -2020 Task 2: Exploiting the best of both worlds. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.46

2020 doi
[195]

Phonemer at WNUT -2020 Task 2: Sequence Classification Using COVID T witter BERT and Bagging Ensemble Technique based on Plurality Voting

Wadhawan, Anshul. Phonemer at WNUT -2020 Task 2: Sequence Classification Using COVID T witter BERT and Bagging Ensemble Technique based on Plurality Voting. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.47

2020 doi
[196]

CXP 949 at WNUT -2020 Task 2: Extracting Informative COVID -19 Tweets - R o BERT a Ensembles and The Continued Relevance of Handcrafted Features

Perrio, Calum and Tayyar Madabushi, Harish. CXP 949 at WNUT -2020 Task 2: Extracting Informative COVID -19 Tweets - R o BERT a Ensembles and The Continued Relevance of Handcrafted Features. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:...

2020 doi
[197]

I nfo M iner at WNUT -2020 Task 2: Transformer-based Covid-19 Informative Tweet Extraction

Hettiarachchi, Hansi and Ranasinghe, Tharindu. I nfo M iner at WNUT -2020 Task 2: Transformer-based Covid-19 Informative Tweet Extraction. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.49

2020 doi
[198]

BANANA at WNUT -2020 Task 2: Identifying COVID -19 Information on T witter by Combining Deep Learning and Transfer Learning Models

Huynh, Tin and Thanh Luan, Luan and Luu, Son T. BANANA at WNUT -2020 Task 2: Identifying COVID -19 Information on T witter by Combining Deep Learning and Transfer Learning Models. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v...

2020 doi
[199]

DATAMAFIA at WNUT -2020 T ask 2: A S tudy of P re-trained L anguage M odels along with R egularization T echniques for D ownstream T asks

Sengupta, Ayan. DATAMAFIA at WNUT -2020 T ask 2: A S tudy of P re-trained L anguage M odels along with R egularization T echniques for D ownstream T asks. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.51

2020 doi
[200]

UP enn HLP at WNUT -2020 Task 2 : Transformer models for classification of COVID 19 posts on T witter

Magge, Arjun and Pimpalkhute, Varad and Rallapalli, Divya and Siguenza, David and Gonzalez-Hernandez, Graciela. UP enn HLP at WNUT -2020 Task 2 : Transformer models for classification of COVID 19 posts on T witter. Proceedings of the Sixth Workshop on Noisy User-generated Text...

2020 doi
[201]

UIT - HSE at WNUT -2020 Task 2: Exploiting CT - BERT for Identifying COVID -19 Information on the T witter Social Network

Tran, Khiem and Phan, Hao and Nguyen, Kiet and Thuy Nguyen, Ngan Luu. UIT - HSE at WNUT -2020 Task 2: Exploiting CT - BERT for Identifying COVID -19 Information on the T witter Social Network. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. d...

2020 doi
[202]

Emory at WNUT -2020 Task 2: Combining Pretrained Deep Learning Models and Feature Enrichment for Informative Tweet Identification

Guo, Yuting and Ali Al-Garadi, Mohammed and Sarker, Abeed. Emory at WNUT -2020 Task 2: Combining Pretrained Deep Learning Models and Feature Enrichment for Informative Tweet Identification. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:...

2020 doi
[203]

CSECU - DSG at WNUT -2020 Task 2: Exploiting Ensemble of Transfer Learning and Hand-crafted Features for Identification of Informative COVID -19 E nglish Tweets

Tasneem, Fareen and Naim, Jannatun and Tasnia, Radiathun and Hossain, Tashin and Chy, Abu Nowshed. CSECU - DSG at WNUT -2020 Task 2: Exploiting Ensemble of Transfer Learning and Hand-crafted Features for Identification of Informative COVID -19 E nglish Tweets. Proceedings of t...

2020 doi
[204]

IRL ab@ IITBHU at WNUT -2020 Task 2: Identification of informative COVID -19 E nglish Tweets using BERT

Chanda, Supriya and Nandy, Eshita and Pal, Sukomal. IRL ab@ IITBHU at WNUT -2020 Task 2: Identification of informative COVID -19 E nglish Tweets using BERT. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.56

2020 doi
[205]

N ut C racker at WNUT -2020 Task 2: Robustly Identifying Informative COVID -19 Tweets using Ensembling and Adversarial Training

Kumar, Priyanshu and Singh, Aadarsh. N ut C racker at WNUT -2020 Task 2: Robustly Identifying Informative COVID -19 Tweets using Ensembling and Adversarial Training. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.57

2020 doi
[206]

DSC - IIT ISM at WNUT -2020 Task 2: Detection of COVID -19 informative tweets using R o BERT a

Dhana Laxmi, Sirigireddy and Agarwal, Rohit and Sinha, Aman. DSC - IIT ISM at WNUT -2020 Task 2: Detection of COVID -19 informative tweets using R o BERT a. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.58

2020 doi
[207]

Linguist Geeks on WNUT -2020 Task 2: COVID -19 Informative Tweet Identification using Progressive Trained Language Models and Data Augmentation

Awatramani, Vasudev and Kumar, Anupam. Linguist Geeks on WNUT -2020 Task 2: COVID -19 Informative Tweet Identification using Progressive Trained Language Models and Data Augmentation. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.186...

2020 doi
[208]

NLPRL at WNUT -2020 Task 2: ELM o-based System for Identification of COVID -19 Tweets

Mundotiya, Rajesh Kumar and Baruah, Rupjyoti and Srivastava, Bhavana and Singh, Anil Kumar. NLPRL at WNUT -2020 Task 2: ELM o-based System for Identification of COVID -19 Tweets. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1...

2020 doi
[209]

SU - NLP at WNUT -2020 Task 2: The Ensemble Models

Fayoumi, Kenan and Yeniterzi, Reyyan. SU - NLP at WNUT -2020 Task 2: The Ensemble Models. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.61

2020 doi
[210]

IDSOU at WNUT -2020 Task 2: Identification of Informative COVID -19 E nglish Tweets

Ohashi, Sora and Kajiwara, Tomoyuki and Chu, Chenhui and Takemura, Noriko and Nakashima, Yuta and Nagahara, Hajime. IDSOU at WNUT -2020 Task 2: Identification of Informative COVID -19 E nglish Tweets. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)....

2020 doi
[211]

C omplex D ata L ab at W - NUT 2020 Task 2: Detecting Informative COVID -19 Tweets by Attending over Linked Documents

Pelrine, Kellin and Danovitch, Jacob and Camacho, Albert Orozco and Rabbany, Reihaneh. C omplex D ata L ab at W - NUT 2020 Task 2: Detecting Informative COVID -19 Tweets by Attending over Linked Documents. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2...

2020 doi
[212]

NEU at WNUT -2020 Task 2: Data Augmentation To Tell BERT That Death Is Not Necessarily Informative

Chauhan, Kumud. NEU at WNUT -2020 Task 2: Data Augmentation To Tell BERT That Death Is Not Necessarily Informative. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.64

2020 doi
[213]

L ynyrd S kynyrd at WNUT -2020 Task 2: Semi-Supervised Learning for Identification of Informative COVID -19 E nglish Tweets

Sancheti, Abhilasha and Chawla, Kushal and Verma, Gaurav. L ynyrd S kynyrd at WNUT -2020 Task 2: Semi-Supervised Learning for Identification of Informative COVID -19 E nglish Tweets. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.1865...

2020 doi
[214]

NIT \_ COVID -19 at WNUT -2020 Task 2: Deep Learning Model R o BERT a for Identify Informative COVID -19 E nglish Tweets

M S, Jagadeesh and P J A, Alphonse. NIT \_ COVID -19 at WNUT -2020 Task 2: Deep Learning Model R o BERT a for Identify Informative COVID -19 E nglish Tweets. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.66

2020 doi
[215]

E dinburgh NLP at WNUT -2020 Task 2: Leveraging Transformers with Generalized Augmentation for Identifying Informativeness in COVID -19 Tweets

Maveli, Nickil. E dinburgh NLP at WNUT -2020 Task 2: Leveraging Transformers with Generalized Augmentation for Identifying Informativeness in COVID -19 Tweets. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.67

2020 doi
[216]

\# GCDH at WNUT -2020 Task 2: BERT -Based Models for the Detection of Informativeness in E nglish COVID -19 Related Tweets

Varachkina, Hanna and Ziehe, Stefan and D. \# GCDH at WNUT -2020 Task 2: BERT -Based Models for the Detection of Informativeness in E nglish COVID -19 Related Tweets. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.68

2020 doi
[217]

Not- NUT s at WNUT -2020 Task 2: A BERT -based System in Identifying Informative COVID -19 E nglish Tweets

Hoang, Thai and Vu, Phuong. Not- NUT s at WNUT -2020 Task 2: A BERT -based System in Identifying Informative COVID -19 E nglish Tweets. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.69

2020 doi
[218]

CIA \_ NITT at WNUT -2020 Task 2: Classification of COVID -19 Tweets Using Pre-trained Language Models

Prakash Babu, Yandrapati and Eswari, Rajagopal. CIA \_ NITT at WNUT -2020 Task 2: Classification of COVID -19 Tweets Using Pre-trained Language Models. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.70

2020 doi
[219]

UET at WNUT -2020 Task 2: A Study of Combining Transfer Learning Methods for Text Classification with R o BERT a

Dao Quang, Huy and Nguyen Minh, Tam. UET at WNUT -2020 Task 2: A Study of Combining Transfer Learning Methods for Text Classification with R o BERT a. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.71

2020 doi
[220]

D artmouth CS at WNUT -2020 Task 2: Informative COVID -19 Tweet Classification Using BERT

Whang, Dylan and Vosoughi, Soroush. D artmouth CS at WNUT -2020 Task 2: Informative COVID -19 Tweet Classification Using BERT. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.72

2020 doi
[221]

S un B ear at WNUT -2020 Task 2: Improving BERT -Based Noisy Text Classification with Knowledge of the Data domain

Doan Bao, Linh and Nguyen, Viet Anh and Pham Huu, Quang. S un B ear at WNUT -2020 Task 2: Improving BERT -Based Noisy Text Classification with Knowledge of the Data domain. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020....

2020 doi
[222]

ISWARA at WNUT -2020 Task 2: Identification of Informative COVID -19 E nglish Tweets using BERT and F ast T ext Embeddings

Putri, Wava Carissa and Hidayat, Rani Aulia and Khasanah, Isnaini Nurul and Mahendra, Rahmad. ISWARA at WNUT -2020 Task 2: Identification of Informative COVID -19 E nglish Tweets using BERT and F ast T ext Embeddings. Proceedings of the Sixth Workshop on Noisy User-generated T...

2020 doi
[223]

COVCOR 20 at WNUT -2020 Task 2: An Attempt to Combine Deep Learning and Expert rules

H. COVCOR 20 at WNUT -2020 Task 2: An Attempt to Combine Deep Learning and Expert rules. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.75

2020 doi
[224]

TEST \_ POSITIVE at W - NUT 2020 Shared Task-3: Cross-task modeling

Chen, Chacha and Huang, Chieh-Yang and Hou, Yaqi and Shi, Yang and Dai, Enyan and Wang, Jiaqi. TEST \_ POSITIVE at W - NUT 2020 Shared Task-3: Cross-task modeling. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.76

2020 doi
[225]

imec- ETRO - VUB at W - NUT 2020 Shared Task-3: A multilabel BERT -based system for predicting COVID -19 events

Yang, Xiangyu and Bekoulis, Giannis and Deligiannis, Nikos. imec- ETRO - VUB at W - NUT 2020 Shared Task-3: A multilabel BERT -based system for predicting COVID -19 events. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020....

2020 doi
[226]

UCD - CS at W - NUT 2020 Shared Task-3: A Text to Text Approach for COVID -19 Event Extraction on Social Media

Wang, Congcong and Lillis, David. UCD - CS at W - NUT 2020 Shared Task-3: A Text to Text Approach for COVID -19 Event Extraction on Social Media. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.78

2020 doi
[227]

Winners at W - NUT 2020 Shared Task-3: Leveraging Event Specific and Chunk Span information for Extracting COVID Entities from Tweets

Kaushal, Ayush and Vaidhya, Tejas. Winners at W - NUT 2020 Shared Task-3: Leveraging Event Specific and Chunk Span information for Extracting COVID Entities from Tweets. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.79

2020 doi
[228]

HLTRI at W - NUT 2020 Shared Task-3: COVID -19 Event Extraction from T witter Using Multi-Task Hopfield Pooling

Weinzierl, Maxwell and Harabagiu, Sanda. HLTRI at W - NUT 2020 Shared Task-3: COVID -19 Event Extraction from T witter Using Multi-Task Hopfield Pooling. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.80

2020 doi
[229]

Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[230]

Findings of the 2020 Conference on Machine Translation ( WMT 20)

Barrault, Lo. Findings of the 2020 Conference on Machine Translation ( WMT 20). Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[231]

Findings of the First Shared Task on Lifelong Learning Machine Translation

Barrault, Lo. Findings of the First Shared Task on Lifelong Learning Machine Translation. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[232]

Amin and Lopes, Ant \'o nio V

Farajian, M. Amin and Lopes, Ant \'o nio V. and Martins, Andr \'e F. T. and Maruf, Sameen and Haffari, Gholamreza. Findings of the WMT 2020 Shared Task on Chat Translation. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[233]

Findings of the WMT 2020 Shared Task on Machine Translation Robustness

Specia, Lucia and Li, Zhenhao and Pino, Juan and Chaudhary, Vishrav and Guzm \'a n, Francisco and Neubig, Graham and Durrani, Nadir and Belinkov, Yonatan and Koehn, Philipp and Sajjad, Hassan and Michel, Paul and Li, Xian. Findings of the WMT 2020 Shared Task on Machine Transl...

2020
[234]

The U niversity of E dinburgh ' s E nglish- T amil and E nglish- I nuktitut Submissions to the WMT 20 News Translation Task

Bawden, Rachel and Birch, Alexandra and Dobreva, Radina and Oncevay, Arturo and Miceli Barone, Antonio Valerio and Williams, Philip. The U niversity of E dinburgh ' s E nglish- T amil and E nglish- I nuktitut Submissions to the WMT 20 News Translation Task. Proceedings of the ...

2020
[235]

GTCOM Neural Machine Translation Systems for WMT 20

Bei, Chao and Zong, Hao and Liu, Qingmin and Yuan, Conghu. GTCOM Neural Machine Translation Systems for WMT 20. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[236]

D i D i ' s Machine Translation System for WMT 2020

Chen, Tanfang and Wang, Weiwei and Wei, Wenyang and Shi, Xing and Li, Xiangang and Ye, Jieping and Knight, Kevin. D i D i ' s Machine Translation System for WMT 2020. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[237]

F acebook AI ' s WMT 20 News Translation Task Submission

Chen, Peng-Jen and Lee, Ann and Wang, Changhan and Goyal, Naman and Fan, Angela and Williamson, Mary and Gu, Jiatao. F acebook AI ' s WMT 20 News Translation Task Submission. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[238]

Linguistically Motivated Subwords for E nglish- T amil Translation: U niversity of G roningen ' s Submission to WMT -2020

Dhar, Prajit and Bisazza, Arianna and van Noord, Gertjan. Linguistically Motivated Subwords for E nglish- T amil Translation: U niversity of G roningen ' s Submission to WMT -2020. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[239]

and Fonollosa, Jos \'e A

Escolano, Carlos and Costa-juss \`a , Marta R. and Fonollosa, Jos \'e A. R. The TALP - UPC System Description for WMT 20 News Translation Task: Multilingual Adaptation for Low Resource MT. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[240]

An Iterative Knowledge Transfer NMT System for WMT 20 News Translation Task

Kim, Jiwan and Park, Soyoon and Kim, Sangha and Choi, Yoonjung. An Iterative Knowledge Transfer NMT System for WMT 20 News Translation Task. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[241]

Tohoku- AIP - NTT at WMT 2020 News Translation Task

Kiyono, Shun and Ito, Takumi and Konno, Ryuto and Morishita, Makoto and Suzuki, Jun. Tohoku- AIP - NTT at WMT 2020 News Translation Task. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[242]

NRC Systems for the 2020 I nuktitut- E nglish News Translation Task

Knowles, Rebecca and Stewart, Darlene and Larkin, Samuel and Littell, Patrick. NRC Systems for the 2020 I nuktitut- E nglish News Translation Task. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[243]

CUNI Submission for the I nuktitut Language in WMT News 2020

Kocmi, Tom. CUNI Submission for the I nuktitut Language in WMT News 2020. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[244]

Tilde at WMT 2020: News Task Systems

Kri s lauks, Rihards and Pinnis, M \=a rcis. Tilde at WMT 2020: News Task Systems. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[245]

S amsung R & D Institute P oland submission to WMT 20 News Translation Task

Krubi \'n ski, Mateusz and Chochowski, Marcin and Boczek, Bart omiej and Koszowski, Miko aj and Dobrowolski, Adam and Szyma \'n ski, Marcin and Przybysz, Pawe. S amsung R & D Institute P oland submission to WMT 20 News Translation Task. Proceedings of the Fifth Conference on M...

2020
[246]

Speed-optimized, Compact Student Models that Distill Knowledge from a Larger Teacher Model: the UEDIN - CUNI Submission to the WMT 2020 News Translation Task

Germann, Ulrich and Grundkiewicz, Roman and Popel, Martin and Dobreva, Radina and Bogoychev, Nikolay and Heafield, Kenneth. Speed-optimized, Compact Student Models that Distill Knowledge from a Larger Teacher Model: the UEDIN - CUNI Submission to the WMT 2020 News Translation ...

2020
[247]

The U niversity of E dinburgh ' s submission to the G erman-to- E nglish and E nglish-to- G erman Tracks in the WMT 2020 News Translation and Zero-shot Translation Robustness Tasks

Germann, Ulrich. The U niversity of E dinburgh ' s submission to the G erman-to- E nglish and E nglish-to- G erman Tracks in the WMT 2020 News Translation and Zero-shot Translation Robustness Tasks. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[248]

Contact Relatedness can help improve multilingual NMT : M icrosoft STCI - MT @ WMT 20

Goyal, Vikrant and Kunchukuttan, Anoop and Kejriwal, Rahul and Jain, Siddharth and Bhagwat, Amit. Contact Relatedness can help improve multilingual NMT : M icrosoft STCI - MT @ WMT 20. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[249]

The AFRL WMT 20 News Translation Systems

Gwinnup, Jeremy and Anderson, Tim. The AFRL WMT 20 News Translation Systems. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[250]

The Ubiqus E nglish- I nuktitut System for WMT 20

Hernandez, Fran c ois and Nguyen, Vincent. The Ubiqus E nglish- I nuktitut System for WMT 20. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[251]

SJTU - NICT ' s Supervised and Unsupervised Neural Machine Translation Systems for the WMT 20 News Translation Task

Li, Zuchao and Zhao, Hai and Wang, Rui and Chen, Kehai and Utiyama, Masao and Sumita, Eiichiro. SJTU - NICT ' s Supervised and Unsupervised Neural Machine Translation Systems for the WMT 20 News Translation Task. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[252]

Combination of Neural Machine Translation Systems at WMT 20

Marie, Benjamin and Rubino, Raphael and Fujita, Atsushi. Combination of Neural Machine Translation Systems at WMT 20. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[253]

W e C hat Neural Machine Translation Systems for WMT 20

Meng, Fandong and Yan, Jianhao and Liu, Yijin and Gao, Yuan and Zeng, Xianfeng and Zeng, Qinsong and Li, Peng and Chen, Ming and Zhou, Jie and Liu, Sifan and Zhou, Hao. W e C hat Neural Machine Translation Systems for WMT 20. Proceedings of the Fifth Conference on Machine Tran...

2020
[254]

PROMT Systems for WMT 2020 Shared News Translation Task

Molchanov, Alexander. PROMT Systems for WMT 2020 Shared News Translation Task. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[255]

e T ranslation ' s Submissions to the WMT 2020 News Translation Task

Oravecz, Csaba and Bontcheva, Katina and Tihanyi, L \'a szl \'o and Kolovratnik, David and Bhaskar, Bhavani and Lardilleux, Adrien and Klocek, Szymon and Eisele, Andreas. e T ranslation ' s Submissions to the WMT 2020 News Translation Task. Proceedings of the Fifth Conference ...

2020
[256]

The ADAPT System Description for the WMT 20 News Translation Task

Parthasarathy, Venkatesh and Ramesh, Akshai and Haque, Rejwanul and Way, Andy. The ADAPT System Description for the WMT 20 News Translation Task. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[257]

CUNI E nglish- C zech and E nglish- P olish Systems in WMT 20: Robust Document-Level Training

Popel, Martin. CUNI E nglish- C zech and E nglish- P olish Systems in WMT 20: Robust Document-Level Training. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[258]

Machine Translation for E nglish -- I nuktitut with Segmentation, Data Acquisition and Pre-Training

Roest, Christian and Edman, Lukas and Minnema, Gosse and Kelly, Kevin and Spenader, Jennifer and Toral, Antonio. Machine Translation for E nglish -- I nuktitut with Segmentation, Data Acquisition and Pre-Training. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[259]

OPPO ' s Machine Translation Systems for WMT 20

Shi, Tingxun and Zhao, Shiyu and Li, Xiaopu and Wang, Xiaoxue and Zhang, Qian and Ai, Di and Dang, Dawei and Zhengshan, Xue and Hao, Jie. OPPO ' s Machine Translation Systems for WMT 20. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[260]

HW - TSC ' s Participation in the WMT 2020 News Translation Shared Task

Wei, Daimeng and Shang, Hengchao and Wu, Zhanglin and Yu, Zhengzhe and Li, Liangyou and Guo, Jiaxin and Wang, Minghan and Yang, Hao and Lei, Lizhi and Qin, Ying and Sun, Shiliang. HW - TSC ' s Participation in the WMT 2020 News Translation Shared Task. Proceedings of the Fifth...

2020
[261]

IIE ' s Neural Machine Translation Systems for WMT 20

Wei, Xiangpeng and Guo, Ping and Li, Yunpeng and Zhang, Xingsheng and Xing, Luxi and Hu, Yue. IIE ' s Neural Machine Translation Systems for WMT 20. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[262]

The Volctrans Machine Translation System for WMT 20

Wu, Liwei and Pan, Xiao and Lin, Zehui and Zhu, Yaoming and Wang, Mingxuan and Li, Lei. The Volctrans Machine Translation System for WMT 20. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[263]

Tencent Neural Machine Translation Systems for the WMT 20 News Translation Task

Wu, Shuangzhi and Wang, Xing and Wang, Longyue and Liu, Fangxu and Xie, Jun and Tu, Zhaopeng and Shi, Shuming and Li, Mu. Tencent Neural Machine Translation Systems for the WMT 20 News Translation Task. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[264]

R ussian- E nglish Bidirectional Machine Translation System

Xv, Ariel. R ussian- E nglish Bidirectional Machine Translation System. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[265]

The D eep M ind C hinese -- E nglish Document Translation System at WMT 2020

Yu, Lei and Sartran, Laurent and Huang, Po-Sen and Stokowiec, Wojciech and Donato, Domenic and Srinivasan, Srivatsan and Andreev, Alek and Ling, Wang and Mokra, Sona and Dal Lago, Agustin and Doron, Yotam and Young, Susannah and Blunsom, Phil and Dyer, Chris. The D eep M ind C...

2020
[266]

The N iu T rans Machine Translation Systems for WMT 20

Zhang, Yuhao and Wang, Ziyang and Cao, Runzhe and Wei, Binghao and Shan, Weiqiao and Zhou, Shuhan and Reheman, Abudurexiti and Zhou, Tao and Zeng, Xin and Wang, Laohu and Mu, Yongyu and Zhang, Jingnan and Liu, Xiaoqian and Zhou, Xuanjun and Li, Yinqiao and Li, Bei and Xiao, To...

2020
[267]

Fine-grained linguistic evaluation for state-of-the-art Machine Translation

Avramidis, Eleftherios and Macketanz, Vivien and Strohriegel, Ursula and Burchardt, Aljoscha and M. Fine-grained linguistic evaluation for state-of-the-art Machine Translation. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[268]

Gender Coreference and Bias Evaluation at WMT 2020

Kocmi, Tom and Limisiewicz, Tomasz and Stanovsky, Gabriel. Gender Coreference and Bias Evaluation at WMT 2020. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[269]

The MUCOW word sense disambiguation test suite at WMT 2020

Scherrer, Yves and Raganato, Alessandro and Tiedemann, J. The MUCOW word sense disambiguation test suite at WMT 2020. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[270]

WMT 20 Document-Level Markable Error Exploration

Zouhar, Vil \'e m and Vojt e chov \'a , Tereza and Bojar, Ond r ej. WMT 20 Document-Level Markable Error Exploration. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[271]

Translating Similar Languages: Role of Mutual Intelligibility in Multilingual Transformers

Adebara, Ife and Nagoudi, El Moatez Billah and Abdul Mageed, Muhammad. Translating Similar Languages: Role of Mutual Intelligibility in Multilingual Transformers. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[272]

Attention Transformer Model for Translation of Similar Languages

Dhanani, Farhan and Rafi, Muhammad. Attention Transformer Model for Translation of Similar Languages. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[273]

Transformer-based Neural Machine Translation System for H indi -- M arathi: WMT 20 Shared Task

Kumar, Amit and Baruah, Rupjyoti and Mundotiya, Rajesh Kumar and Singh, Anil Kumar. Transformer-based Neural Machine Translation System for H indi -- M arathi: WMT 20 Shared Task. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[274]

H indi- M arathi Cross Lingual Model

Laskar, Sahinur Rahman and Khilji, Abdullah Faiz Ur Rahman and Pakray, Partha and Bandyopadhyay, Sivaji. H indi- M arathi Cross Lingual Model. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[275]

Transfer Learning for Related Languages: Submissions to the WMT 20 Similar Language Translation Task

Madaan, Lovish and Sharma, Soumya and Singla, Parag. Transfer Learning for Related Languages: Submissions to the WMT 20 Similar Language Translation Task. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[276]

and Sidorov, Grigori and Costa-Juss \`a , Marta R

Men \'e ndez-Salazar, Luis A. and Sidorov, Grigori and Costa-Juss \`a , Marta R. The IPN - CIC team system submission for the WMT 2020 similar language task. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[277]

NMT based Similar Language Translation for H indi - M arathi

Mujadia, Vandan and Sharma, Dipti. NMT based Similar Language Translation for H indi - M arathi. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[278]

and Rani, Priya and Bansal, Akanksha and Chakravarthi, Bharathi Raja and Kumar, Ritesh and McCrae, John P

Ojha, Atul Kr. and Rani, Priya and Bansal, Akanksha and Chakravarthi, Bharathi Raja and Kumar, Ritesh and McCrae, John P. NUIG -Panlingua- KMI H indi- M arathi MT Systems for Similar Language Translation Task @ WMT 2020. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[279]

Neural Machine Translation for Similar Languages: The Case of I ndo- A ryan Languages

Pal, Santanu and Zampieri, Marcos. Neural Machine Translation for Similar Languages: The Case of I ndo- A ryan Languages. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[280]

Neural Machine Translation between similar S outh- S lavic languages

Popovi \'c , Maja and Poncelas, Alberto. Neural Machine Translation between similar S outh- S lavic languages. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[281]

Infosys Machine Translation System for WMT 20 Similar Language Translation Task

Rathinasamy, Kamalkumar and Singh, Amanpreet and Sivasambagupta, Balaguru and Prasad Neerchal, Prajna and Sivasankaran, Vani. Infosys Machine Translation System for WMT 20 Similar Language Translation Task. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[282]

Document Level NMT of Low-Resource Languages with Backtranslation

Ul Haq, Sami and Abdul Rauf, Sadaf and Shaukat, Arsalan and Saeed, Abdullah. Document Level NMT of Low-Resource Languages with Backtranslation. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[283]

Costa-juss \`a , Marta

Verg \'e s Boncompte, Pere and R. Costa-juss \`a , Marta. Multilingual Neural Machine Translation: Case-study for C atalan, S panish and P ortuguese R omance Languages. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[284]

A3-108 Machine Translation System for Similar Language Translation Shared Task 2020

Yadav, Saumitra and Shrivastava, Manish. A3-108 Machine Translation System for Similar Language Translation Shared Task 2020. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[285]

The U niversity of M aryland ' s Submissions to the WMT 20 Chat Translation Task: Searching for More Data to Adapt Discourse-Aware Neural Machine Translation

Bao, Calvin and Shiue, Yow-Ting and Song, Chujun and Li, Jie and Carpuat, Marine. The U niversity of M aryland ' s Submissions to the WMT 20 Chat Translation Task: Searching for More Data to Adapt Discourse-Aware Neural Machine Translation. Proceedings of the Fifth Conference ...

2020
[286]

Naver Labs E urope ' s Participation in the Robustness, Chat, and Biomedical Tasks at WMT 2020

Berard, Alexandre and Calapodescu, Ioan and Nikoulina, Vassilina and Philip, Jerin. Naver Labs E urope ' s Participation in the Robustness, Chat, and Biomedical Tasks at WMT 2020. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[287]

The U niversity of E dinburgh- U ppsala U niversity ' s Submission to the WMT 2020 Chat Translation Task

Moghe, Nikita and Hardmeier, Christian and Bawden, Rachel. The U niversity of E dinburgh- U ppsala U niversity ' s Submission to the WMT 2020 Chat Translation Task. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[288]

JUST System for WMT 20 Chat Translation Task

Mohammed, Roweida and Al-Ayyoub, Mahmoud and Abdullah, Malak. JUST System for WMT 20 Chat Translation Task. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[289]

Tencent AI Lab Machine Translation Systems for WMT 20 Chat Translation Task

Wang, Longyue and Tu, Zhaopeng and Wang, Xing and Ding, Li and Ding, Liang and Shi, Shuming. Tencent AI Lab Machine Translation Systems for WMT 20 Chat Translation Task. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[290]

Combining Sequence Distillation and Transfer Learning for Efficient Low-Resource Neural Machine Translation Models

Dabre, Raj and Fujita, Atsushi. Combining Sequence Distillation and Transfer Learning for Efficient Low-Resource Neural Machine Translation Models. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[291]

Fast Interleaved Bidirectional Sequence Generation

Zhang, Biao and Titov, Ivan and Sennrich, Rico. Fast Interleaved Bidirectional Sequence Generation. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[292]

Priming Neural Machine Translation

Pham, Minh Quang and Xu, Jitao and Crego, Josep and Yvon, Fran c ois and Senellart, Jean. Priming Neural Machine Translation. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[293]

Subword Segmentation and a Single Bridge Language Affect Zero-Shot Neural Machine Translation

Rios, Annette and M. Subword Segmentation and a Single Bridge Language Affect Zero-Shot Neural Machine Translation. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[294]

Look It Up: Bilingual and Monolingual Dictionaries Improve Neural Machine Translation

Zhong, Xing Jie and Chiang, David. Look It Up: Bilingual and Monolingual Dictionaries Improve Neural Machine Translation. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[295]

Complete Multilingual Neural Machine Translation

Freitag, Markus and Firat, Orhan. Complete Multilingual Neural Machine Translation. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[296]

Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity

Thompson, Brian and Post, Matt. Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[297]

When Does Unsupervised Machine Translation Work?

Marchisio, Kelly and Duh, Kevin and Koehn, Philipp. When Does Unsupervised Machine Translation Work?. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[298]

Language Models not just for Pre-training: Fast Online Neural Noisy Channel Modeling

Bhosale, Shruti and Yee, Kyra and Edunov, Sergey and Auli, Michael. Language Models not just for Pre-training: Fast Online Neural Noisy Channel Modeling. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[299]

Towards Multimodal Simultaneous Neural Machine Translation

Imankulova, Aizhan and Kaneko, Masahiro and Hirasawa, Tosho and Komachi, Mamoru. Towards Multimodal Simultaneous Neural Machine Translation. Proceedings of the Fifth Conference on Machine Translation. 2020

2020
[300]

Diving Deep into Context-Aware Neural Machine Translation

Huo, Jingjing and Herold, Christian and Gao, Yingbo and Dahlmann, Leonard and Khadivi, Shahram and Ney, Hermann. Diving Deep into Context-Aware Neural Machine Translation. Proceedings of the Fifth Conference on Machine Translation. 2020

2020

Pith tools

Reviewed May 11, 2026 · model on record in the stance chip above.

[1] [1]

Roy Bar-Haim, Ido Dagan, Bill Dolan, Lisa Ferro, Danilo Giampiccolo, Bernardo Magnini, and Idan Szpektor. 2006. The second PASCAL recognising textual entailment challenge. In Proceedings of the second PASCAL challenges workshop on recognising textual entailment, volume 6, pages 6--4. Venice

work page 2006

[2] [2]

Luisa Bentivogli, Peter Clark, Ido Dagan, and Danilo Giampiccolo. 2009. The fifth PASCAL recognizing textual entailment challenge. In TAC

work page 2009

[3] [3]

James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake Vander P las, Skye Wanderman- M ilne, and Qiao Zhang. 2018. http://github.com/google/jax JAX : composable transformations of P ython+ N um P y programs

work page 2018

[4] [4]

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gr...

work page 2020

[5] [5]

Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. 2019. https://doi.org/10.18653/v1/N19-1300 B ool Q : Exploring the surprising difficulty of natural yes/no questions . In Proceedings of the 2019 Conference of the North A merican Chapter of the Association for Computational Linguistics: Human Language...

work page doi:10.18653/v1/n19-1300 2019

[6] [6]

Ido Dagan, Oren Glickman, and Bernardo Magnini. 2005. The PASCAL recognising textual entailment challenge. In Machine Learning Challenges Workshop, pages 177--190. Springer

work page 2005

[7] [7]

Marie-Catherine De Marneff, Mandy Simons, and Judith Tonhauser. 2019. The CommitmentBank : Investigating projection in naturally occurring discourse. Proceedings of Sinn und Bedeutung 23

work page 2019

[8] [8]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. https://doi.org/10.18653/v1/N19-1423 BERT : Pre-training of deep bidirectional transformers for language understanding . In Proceedings of the 2019 Conference of the North A merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long a...

work page doi:10.18653/v1/n19-1423 2019