NarrativeTime is a timeline annotation framework achieving full TLink coverage, shown via re-annotation of TimeBankDense with comparable agreement and higher density plus a new TimeBankNT corpus.
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FIESTA uses bandit algorithms to adaptively decide how many seeds and splits to run for each candidate model, focusing effort on promising ones while providing guarantees on selecting the optimal model.
Across 28 languages, an information-theoretic irregularity score derived from neural transduction models correlates positively with frequency, with stronger effects when aggregated over paradigms.
A fully differentiable parser that stochastically samples projective dependency trees using Gumbel perturbations and dynamic programming to boost downstream task performance without direct supervision.
Introduces a new English dataset from r/AskParents and r/needadvice annotated for advice sentences plus preliminary models showing pre-trained LMs outperform rule-based systems but the task remains challenging.
Gated lexical shortcut connections added to the transformer yield 0.9 BLEU average gains on five WMT directions while lowering the lexical content stored in hidden states.
The first shared task on MT robustness received 23 submissions showing up to +22.33 BLEU gains on noisy Reddit data, with strong human-BLEU correlation.
A compositional neural semantic parser achieves competitive accuracies across diverse graphbanks for the first time and sets new state-of-the-art results on DM, PAS, PSD, AMR 2015 and EDS when combined with BERT and multi-task learning.
Gated fusion of fastText and BERT embeddings into an end-to-end ASR model captures multi-sentence conversational context and lowers word error rate on the Switchboard corpus.
Saliency-driven interpretation methods reveal that NMT models learn word alignments of better quality than fast-align under force decoding and consistent with automatic tools under free decoding.
Authors release a new 800-sentence gender-balanced profession dataset and use it to test occupational gender stereotypes in three sentiment analysis models.
Contextual embeddings are propagated through WordNet to produce full-coverage sense representations that let a simple k-NN classifier outperform prior neural WSD models.
Reinforce-NAT and FS-decoder retrieve target sequential information for non-autoregressive translation, yielding higher BLEU than baseline NAT while preserving fast decoding and approaching autoregressive quality.
Creates datasets and an extraction model that identifies task-dataset-metric-score information in NLP papers to support automatic leaderboard construction.
A multimodal Transformer ingests image features plus multiple external entity label sources and learns to control their appearance in fluent output captions.
Word deletion impact on BERT embeddings is measured to estimate syntactic reducibility of words and n-grams, then applied to induce dependency trees.
Introduces a weakly-supervised framework partitioning CTA transcript parsing into sequence labeling and text span-pair relation extraction using distant supervision from protocols and neighbor sentences for long-range context.
Compares LIME, input perturbation and attention for explaining QA on KB+text; proposes automatic evaluation paradigm and finds input perturbation superior in both automatic and human studies.
Multimodal training with attention and contrastive multi-view learning improves both combined and acoustic-only emotion recognition on IEMOCAP over prior acoustic baselines.
Re-ranking conversational responses with event causality and role-factored tensor embeddings improves coherency and dialogue continuity.
DAL combines dual learning on query-response pairs with adversarial training to improve diversity and naturalness in generated dialogue responses over prior methods.
A label consistency training framework improves F1 on the ProPara benchmark for procedural text comprehension by using multiple independent descriptions of the same process.
Multi-task learning with BIO tag embeddings from NER yields over 10% absolute F1 gain and beats prior SOTA on ACE 2005 Chinese and English relation extraction.
LASER sentence embeddings are applied directly to filter parallel corpora, achieving the best BLEU scores in the WMT19 low-resource tasks for Nepali-English and Sinhala-English by margins of 1.3 and 1.4.
citing papers explorer
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NarrativeTime: Dense Temporal Annotation on a Timeline
NarrativeTime is a timeline annotation framework achieving full TLink coverage, shown via re-annotation of TimeBankDense with comparable agreement and higher density plus a new TimeBankNT corpus.
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FIESTA: Fast IdEntification of State-of-The-Art models using adaptive bandit algorithms
FIESTA uses bandit algorithms to adaptively decide how many seeds and splits to run for each candidate model, focusing effort on promising ones while providing guarantees on selecting the optimal model.
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Morphological Irregularity Correlates with Frequency
Across 28 languages, an information-theoretic irregularity score derived from neural transduction models correlates positively with frequency, with stronger effects when aggregated over paradigms.
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Learning Latent Trees with Stochastic Perturbations and Differentiable Dynamic Programming
A fully differentiable parser that stochastically samples projective dependency trees using Gumbel perturbations and dynamic programming to boost downstream task performance without direct supervision.
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Help! Need Advice on Identifying Advice
Introduces a new English dataset from r/AskParents and r/needadvice annotated for advice sentences plus preliminary models showing pre-trained LMs outperform rule-based systems but the task remains challenging.
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Widening the Representation Bottleneck in Neural Machine Translation with Lexical Shortcuts
Gated lexical shortcut connections added to the transformer yield 0.9 BLEU average gains on five WMT directions while lowering the lexical content stored in hidden states.
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Findings of the First Shared Task on Machine Translation Robustness
The first shared task on MT robustness received 23 submissions showing up to +22.33 BLEU gains on noisy Reddit data, with strong human-BLEU correlation.
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Compositional Semantic Parsing Across Graphbanks
A compositional neural semantic parser achieves competitive accuracies across diverse graphbanks for the first time and sets new state-of-the-art results on DM, PAS, PSD, AMR 2015 and EDS when combined with BERT and multi-task learning.
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Gated Embeddings in End-to-End Speech Recognition for Conversational-Context Fusion
Gated fusion of fastText and BERT embeddings into an end-to-end ASR model captures multi-sentence conversational context and lowers word error rate on the Switchboard corpus.
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Saliency-driven Word Alignment Interpretation for Neural Machine Translation
Saliency-driven interpretation methods reveal that NMT models learn word alignments of better quality than fast-align under force decoding and consistent with automatic tools under free decoding.
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Good Secretaries, Bad Truck Drivers? Occupational Gender Stereotypes in Sentiment Analysis
Authors release a new 800-sentence gender-balanced profession dataset and use it to test occupational gender stereotypes in three sentiment analysis models.
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Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense Disambiguation
Contextual embeddings are propagated through WordNet to produce full-coverage sense representations that let a simple k-NN classifier outperform prior neural WSD models.
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Retrieving Sequential Information for Non-Autoregressive Neural Machine Translation
Reinforce-NAT and FS-decoder retrieve target sequential information for non-autoregressive translation, yielding higher BLEU than baseline NAT while preserving fast decoding and approaching autoregressive quality.
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Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction
Creates datasets and an extraction model that identifies task-dataset-metric-score information in NLP papers to support automatic leaderboard construction.
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Informative Image Captioning with External Sources of Information
A multimodal Transformer ingests image features plus multiple external entity label sources and learns to control their appearance in fluent output captions.
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Inducing Syntactic Trees from BERT Representations
Word deletion impact on BERT embeddings is measured to estimate syntactic reducibility of words and n-grams, then applied to induce dependency trees.
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Eliciting Knowledge from Experts:Automatic Transcript Parsing for Cognitive Task Analysis
Introduces a weakly-supervised framework partitioning CTA transcript parsing into sequence labeling and text span-pair relation extraction using distant supervision from protocols and neighbor sentences for long-range context.
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Interpretable Question Answering on Knowledge Bases and Text
Compares LIME, input perturbation and attention for explaining QA on KB+text; proposes automatic evaluation paradigm and finds input perturbation superior in both automatic and human studies.
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Multimodal and Multi-view Models for Emotion Recognition
Multimodal training with attention and contrastive multi-view learning improves both combined and acoustic-only emotion recognition on IEMOCAP over prior acoustic baselines.
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Conversational Response Re-ranking Based on Event Causality and Role Factored Tensor Event Embedding
Re-ranking conversational responses with event causality and role-factored tensor embeddings improves coherency and dialogue continuity.
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DAL: Dual Adversarial Learning for Dialogue Generation
DAL combines dual learning on query-response pairs with adversarial training to improve diversity and naturalness in generated dialogue responses over prior methods.
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Be Consistent! Improving Procedural Text Comprehension using Label Consistency
A label consistency training framework improves F1 on the ProPara benchmark for procedural text comprehension by using multiple independent descriptions of the same process.
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Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data
Multi-task learning with BIO tag embeddings from NER yields over 10% absolute F1 gain and beats prior SOTA on ACE 2005 Chinese and English relation extraction.
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Low-Resource Corpus Filtering using Multilingual Sentence Embeddings
LASER sentence embeddings are applied directly to filter parallel corpora, achieving the best BLEU scores in the WMT19 low-resource tasks for Nepali-English and Sinhala-English by margins of 1.3 and 1.4.
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Adversarial Regularization for Visual Question Answering: Strengths, Shortcomings, and Side Effects
Adversarial regularization improves VQA performance on out-of-domain bias tests but introduces unstable gradients, reduced in-domain accuracy, and over-reliance on visual cues at the expense of linguistic information.
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Learning Compressed Sentence Representations for On-Device Text Processing
Four binarization strategies turn continuous sentence embeddings into binary form, cutting storage by over 98% with only about 2% performance drop on downstream tasks.
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Supervised Contextual Embeddings for Transfer Learning in Natural Language Processing Tasks
Extracting representations from pre-trained supervised models enriches word embeddings with task and domain knowledge, improving transfer learning in cross-task, cross-domain, and cross-lingual NLP settings particularly under low-resource conditions.
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Enhancing PIO Element Detection in Medical Text Using Contextualized Embedding
Builds an improved PIO dataset and reports performance gains from domain-specific BERT embeddings plus ensembles in multi-label PIO classification.
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LIAAD at SemDeep-5 Challenge: Word-in-Context (WiC)
An adapted WSD system with contextual and sense embeddings places second in the WiC challenge while avoiding task-specific training data.
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Evaluating the Supervised and Zero-shot Performance of Multi-lingual Translation Models
Task-specific decoder parameters outperform fully shared decoder parameters in both supervised and zero-shot multilingual translation performance.
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Improving Zero-shot Translation with Language-Independent Constraints
Language-independent constraints and regularization in multilingual Transformer NMT yield a 2.23 BLEU average gain on zero-shot pairs from the IWSLT 2017 dataset.
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Is It Worth the Attention? A Comparative Evaluation of Attention Layers for Argument Unit Segmentation
Attention layers do not improve BiLSTM performance on argument unit segmentation and contextualized embeddings show little benefit.
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Demonstration of a Neural Machine Translation System with Online Learning for Translators
Demonstration of an online-learning NMT system integrated with SDL Trados Studio for continuous adaptation from human post-edits in production.
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Robust Machine Translation with Domain Sensitive Pseudo-Sources: Baidu-OSU WMT19 MT Robustness Shared Task System Report
Baidu-OSU WMT19 system achieves >10 BLEU gain on En-Fr and Fr-En social media translation via domain sensitive training and pseudo noisy sources.
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CUNI System for the WMT19 Robustness Task
A pre-trained Transformer MT system outperforms an LSTM baseline on noisy text and gains further robustness from fine-tuning on noisy data without harming clean-text performance.