Can Reasoning Models Detect Changes to their Chains of Thought?

Chengyuan Xue; Miriam Wanner; Sathvik Napa; Utkarsh Singh; William Walden

REVIEW 2 major objections 1 minor 300 references

Can Reasoning Models Detect Changes to their Chains of Thought?

T0 review · 2 major / 1 minor · reviewed 2026-06-26 · grok-4.3

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

Pith's one-line read Reasoning models detect edits to their chains of thought only modestly.

desk verdict The paper finds modest CoT tampering detection in reasoning models with no own-vs-other difference, but methods details are missing so the numbers are hard to interpret. read the letter →

arxiv 2606.22085 v1 pith:ZIZW65ZR submitted 2026-06-20 cs.AI cs.CLcs.LG

Sathvik Napa , Utkarsh Singh , Chengyuan Xue , Miriam Wanner , William Walden This is my paper · ORCID

classification cs.AIcs.CLcs.LG

keywords chainofthoughtreasoningmodelsmodelinterventiondetectionAIsafetyprefilling

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 tests whether current reasoning models can spot when their chain of thought has been changed by an external intervention. This matters because many proposed uses of AI involve editing the model's reasoning steps to improve safety or performance, and such edits would fail if the model notices and adjusts its output accordingly. The study finds that detection accuracy is low, models cannot usually say what changed, and performance is similar whether the original CoT was their own or someone else's. These results suggest that CoT editing may often go undetected in practice.

What carries the argument

The CoT detection task, in which models receive a reasoning trace that may have been prefilling or altered and are prompted to report whether and how it differs from what they would have generated.

What would settle it

A follow-up experiment that achieves high accuracy in identifying both the presence and the specific nature of CoT modifications across multiple models and edit types would falsify the claim of only modest detection.

Watch

Extended reading notes

Core claim

Recent reasoning models show only modest accuracy at detecting interventions on their chains of thought, whether those interventions occur during or after reasoning and whether the chains come from the model itself or from other models. Models also have difficulty specifying exactly how the chain was modified.

Load-bearing premise

The prompts and edit methods used in the experiments measure genuine detection ability rather than being influenced by how the questions are worded or how the changes are introduced.

Editorial extensions

If this is right

CoT prefilling with stronger models' reasoning can likely proceed without the target model noticing the substitution.
Interventions to remove unsafe reasoning steps may succeed because the model does not flag the alteration.
Detection performance does not improve when the CoT belongs to the model being tested versus another model.
Modest detection holds both while the model is still reasoning and after the chain is complete.

Reading between the lines

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

Developers could use CoT editing as a reliable way to steer model behavior without triggering internal consistency checks.
Future experiments might vary the subtlety of the edits to find the threshold at which detection becomes reliable.
Similar tests on non-reasoning models could reveal whether chain-of-thought use itself affects self-monitoring ability.

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Signed reviews

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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 core result is that current reasoning models catch CoT edits only modestly well, cannot say what changed, and perform about the same on their own traces as on others'. This is a straightforward empirical question with direct relevance to safety edits that rewrite reasoning steps.

The work is new in its focus on recent reasoning models and the specific conditions tested (own vs other CoTs, during vs after reasoning). It frames the safety motivation cleanly and reports consistent patterns across those conditions.

The main limitation is that the abstract supplies no accuracy numbers, sample sizes, statistical tests, or control conditions with zero interventions. Without those, it is impossible to tell whether the modest detection rates reflect real limited awareness or just how the prompts were worded. The stress-test point about base-rate reporting of changes is live here because no such controls are described.

This is useful for alignment researchers who need to know whether CoT edits will stay hidden. It is not yet strong enough for broad citation until the methods and raw results are visible.

A serious editor should send it to review so the experimental design and any controls can be checked properly.

Referee Report

2 major / 1 minor

Summary. The paper investigates whether recent reasoning models can detect edits to their chains of thought (CoTs), both during and after generation and when the CoT is their own or from another model. It reports three main empirical findings: models achieve only modest accuracy in detecting such interventions, struggle to identify the specific type of modification, and show comparable detection performance on their own CoTs versus those of other models.

Significance. If the empirical results are robust, the work provides evidence that CoT interventions (e.g., for safety or capability enhancement) are unlikely to be noticed by the model itself, which bears on the feasibility of such techniques without triggering behavioral changes. The study contributes behavioral data on model introspection and self-monitoring in the context of reasoning traces.

major comments (2)

[Methods] Methods (experimental design): The study does not report control conditions using unmodified CoTs to measure the base rate at which models report 'changes' in the absence of any intervention. Without these controls, the modest detection accuracy cannot be distinguished from generic prompt-induced affirmative responses, undermining interpretation of findings (i) and (iii).
[Results] Results (detection accuracy): The abstract and reported findings provide no sample sizes, statistical tests, confidence intervals, or error bars, making it impossible to assess whether the 'modest accuracy' and 'no difference between own/other' claims are statistically supported or merely descriptive.

minor comments (1)

[Abstract] The abstract refers to 'a variety of conditions' but does not enumerate them; a brief enumeration in the abstract or introduction would improve readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which highlight important aspects of experimental design and reporting. We address each major point below and commit to revisions that strengthen the empirical claims.

read point-by-point responses

Referee: [Methods] The study does not report control conditions using unmodified CoTs to measure the base rate at which models report 'changes' in the absence of any intervention. Without these controls, the modest detection accuracy cannot be distinguished from generic prompt-induced affirmative responses, undermining interpretation of findings (i) and (iii).

Authors: We agree this is a valid concern. The current experiments focus on modified CoTs but do not explicitly include unmodified controls to quantify false-positive rates. In the revised manuscript we will add these control conditions (prompting models to detect changes on their original, unmodified CoTs) and report the resulting base rates alongside the main results. This will allow clearer interpretation of the reported detection accuracies. revision: yes
Referee: [Results] Results (detection accuracy): The abstract and reported findings provide no sample sizes, statistical tests, confidence intervals, or error bars, making it impossible to assess whether the 'modest accuracy' and 'no difference between own/other' claims are statistically supported or merely descriptive.

Authors: We acknowledge the omission. The full manuscript contains the underlying trial counts but does not present sample sizes, statistical tests, confidence intervals, or error bars in the abstract or main result summaries. We will revise to include these details (e.g., N per condition, appropriate tests for accuracy differences, and error bars on figures) so that the strength of the 'modest accuracy' and 'own vs. other' comparisons can be evaluated rigorously. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: purely empirical behavioral study with no derivations or self-referential structure

full rationale

The paper is an empirical investigation of model behavior under CoT interventions. It reports experimental results on detection accuracy, identification of modification type, and own-vs-other CoT performance. No equations, fitted parameters, uniqueness theorems, ansatzes, or derivation chains appear in the provided text. All claims rest on direct measurement of prompted responses rather than any reduction of outputs to inputs by construction. This is a standard non-circular empirical design.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

No mathematical derivations, free parameters, axioms, or invented entities are present; the work is an empirical measurement study.

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Cite this review

Pith. "Pith review of Can Reasoning Models Detect Changes to their Chains of Thought?." pith.science (2026). https://pith.science/paper/ZIZW65ZR

@misc{pith2026260622085,
  author       = {Pith},
  title        = {Pith review of: Can Reasoning Models Detect Changes to their Chains of Thought?},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZIZW65ZR}},
  note         = {Machine review of arXiv:2606.22085}
}

read the original abstract

There are many reasons one may want to edit a model's chain of thought (CoT) -- e.g., to prefill it with reasoning from a stronger model or to remove steps that may yield unsafe outputs. The success of these interventions plausibly depends on a model's inability to notice them, as the model may alter its behavior if it suspects tampering. In this work, we study whether recent reasoning models are able to detect such interventions on their CoTs under a variety of conditions: both during reasoning and after it, and when prefilled both with their own CoTs and with those of other models. Broadly, we find that (i) models exhibit only very modest detection accuracy; (ii) models struggle to identify *how* their CoT was modified; and (iii) models are about as good at detecting changes to their own CoTs as to those of other models.

Figures

Figures reproduced from arXiv: 2606.22085 by the authors.

**Figure 2.** Detection accuracy for the (alerted) completed (dark bars; §4.1) and partial (light bars; §4.2) conditions. The Ione and Erep are shared across conditions; Dhalf and Epar are unique to completed and partial, respectively. Error bars indicate 95% CIs; ∗ denotes significantly different from 50% at α = 0.05 on a two-sided z-test. 4 Detecting Changes Here, we investigate whether models can detect modifications to their … view at source ↗

**Figure 4.** Model accuracy in identifying where a CoT modification was made on MMLU. 5 Localizing Changes We have shown that models have only moderate ability to detect CoT modifications. We now investigate whether they are able to identify where modifications occur. To facilitate clear localization of changes, we make modifications involving only a single step—either inserting (Ione), deleting (Done), or paraphrasing (Eone) … view at source ↗

Figures from the paper (5 more)

**Figure 5.** Figure 5: unalerted results in the completed condition [PITH_FULL_IMAGE:figures/full_fig_p013_5.png]

**Figure 6.** Figure 6: Complete results for the experiment described in § [PITH_FULL_IMAGE:figures/full_fig_p014_6.png]

**Figure 7.** Figure 7: Full change localization accuracy results (§ [PITH_FULL_IMAGE:figures/full_fig_p015_7.png]

**Figure 8.** Figure 8: Effect of replacing the fixed irrelevant statement used for Ione in the main text (left column) with a dynamically LLM-generated statement that is relevant to the problem but that is a non-sequitur (Ione - LLM; right column). Detection accuracy plummets across the boar…

**Figure 9.** Figure 9: Main task accuracy for all models on all datasets. We consistently obtain best results with [PITH_FULL_IMAGE:figures/full_fig_p017_9.png]

Discussion (0). Continue with ORCID to comment.

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Pipelines for Social Bias Testing of Large Language Models

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Foundation Models of Scientific Knowledge for Chemistry: Opportunities, Challenges and Lessons Learned

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Using Item Response Theory to Measure Gender and Racial Bias of a BERT -based Automated E nglish Speech Assessment System

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Mitigating Learnerese Effects for CEFR Classification

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A Baseline Readability Model for C ebuano

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Generation of Synthetic Error Data of Verb Order Errors for S wedish

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A Dependency Treebank of Spoken Second Language E nglish

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Starting from ``Zero'': An Incremental Zero-shot Learning Approach for Assessing Peer Feedback Comments

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On Assessing and Developing Spoken `Grammatical Error Correction' Systems

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Automatic True/False Question Generation for Educational Purpose

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Cross-corpora experiments of automatic proficiency assessment and error detection for spoken E nglish

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Activity focused Speech Recognition of Preschool Children in Early Childhood Classrooms

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Structural information in mathematical formulas for exercise difficulty prediction: a comparison of NLP representations

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The Specificity and Helpfulness of Peer-to-Peer Feedback in Higher Education

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Similarity-Based Content Scoring - How to Make S - BERT Keep Up With BERT

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Don ' t Drop the Topic - The Role of the Prompt in Argument Identification in Student Writing

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ALEN App: Argumentative Writing Support To Foster E nglish Language Learning

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Assessing sentence readability for G erman language learners with broad linguistic modeling or readability formulas: When do linguistic insights make a difference?

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Parametrizable exercise generation from authentic texts: Effectively targeting the language means on the curriculum

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Selecting Context Clozes for Lightweight Reading Compliance

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`Meet me at the ribary' -- Acceptability of spelling variants in free-text answers to listening comprehension prompts

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Educational Tools for Mapuzugun

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An Evaluation of Binary Comparative Lexical Complexity Models

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Toward Automatic Discourse Parsing of Student Writing Motivated by Neural Interpretation

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Educational Multi-Question Generation for Reading Comprehension

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Computationally Identifying Funneling and Focusing Questions in Classroom Discourse

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Response Construct Tagging: NLP -Aided Assessment for Engineering Education

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Incremental Disfluency Detection for Spoken Learner E nglish

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I mage A rg: A Multi-modal Tweet Dataset for Image Persuasiveness Mining

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Data Augmentation for Improving the Prediction of Validity and Novelty of Argumentative Conclusions

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Analyzing Culture-Specific Argument Structures in Learner Essays

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A Unified Representation and a Decoupled Deep Learning Architecture for Argumentation Mining of Students' Persuasive Essays

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Will It Blend? Mixing Training Paradigms & Prompting for Argument Quality Prediction

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Argument Novelty and Validity Assessment via Multitask and Transfer Learning

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Is Your Perspective Also My Perspective? Enriching Prediction with Subjectivity

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Boundary Detection and Categorization of Argument Aspects via Supervised Learning

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Predicting the Presence of Reasoning Markers in Argumentative Text

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Detecting Arguments in CJEU Decisions on Fiscal State Aid

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Multimodal Argument Mining: A Case Study in Political Debates

Mancini, Eleonora and Ruggeri, Federico and Galassi, Andrea and Torroni, Paolo. Multimodal Argument Mining: A Case Study in Political Debates. Proceedings of the 9th Workshop on Argument Mining. 2022

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A Robustness Evaluation Framework for Argument Mining

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On Selecting Training Corpora for Cross-Domain Claim Detection

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Entity-based Claim Representation Improves Fact-Checking of Medical Content in Tweets

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The graphical brain and deep inference

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Investigating the Impact of Different Pivot Languages on Translation Quality

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Predicting the number of errors in human translation using source text and translator characteristics

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Proficiency and External Aides: Impact of Translation Brief and Search Conditions on Post-editing Quality

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Entropy as a measurement of cognitive load in translation

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PEMT human evaluation at 100x scale with risk-driven sampling

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Picking Out The Best MT Model: On The Methodology Of Human Evaluation

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Post-editing of Machine-Translated Patents: High Tech with High Stakes

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The State of the Machine Translation 2022

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The Translation Impact of Global CX

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Improving Consistency of Human and Machine Translations

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Improve MT for Search with Selected Translation Memory using Search Signals

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Data Analytics Meet Machine Translation

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Comparison Between ATA Grading Framework Scores and Auto Scores

Garland, Evelyn and Berger, Carola and Ritzdorf, Jon. Comparison Between ATA Grading Framework Scores and Auto Scores. Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Tra...

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Lingua: Addressing Scenarios for Live Interpretation and Automatic Dubbing

Anderson, Nathan and Wilson, Caleb and Richardson, Stephen D. Lingua: Addressing Scenarios for Live Interpretation and Automatic Dubbing. Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track...

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All You Need is Source! A Study on Source-based Quality Estimation for Neural Machine Translation

Cambra, Jon and Nunziatini, Mara. All You Need is Source! A Study on Source-based Quality Estimation for Neural Machine Translation. Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and ...

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Knowledge Distillation for Sustainable Neural Machine Translation

Jooste, Wandri and Way, Andy and Haque, Rejwanul and Superbo, Riccardo. Knowledge Distillation for Sustainable Neural Machine Translation. Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Trac...

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Business Critical Errors: A Framework for Adaptive Quality Feedback

Stewart, Craig A and Gon c alves, Madalena and Buchicchio, Marianna and Lavie, Alon. Business Critical Errors: A Framework for Adaptive Quality Feedback. Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and...

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A Snapshot into the Possibility of Video Game Machine Translation

Hansen, Damien and Houlmont, Pierre-Yves. A Snapshot into the Possibility of Video Game Machine Translation. Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track). 2022

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Customization options for language pairs without E nglish

Giulianelli, Daniele. Customization options for language pairs without E nglish. Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track). 2022

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Boosting Neural Machine Translation with Similar Translations

Xu, Jitao and Crego, Josep and Senellart, Jean. Boosting Neural Machine Translation with Similar Translations. Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track). 2022

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Feeding NMT a Healthy Diet -- The Impact of Quality, Quantity, or the Right Type of Nutrients

Nasir, Abdallah and Alisis, Sara and Jaikat, Ruba W and Jonsson, Rebecca and Qardan, Sara and Shawahneh, Eyas and Al-Khdour, Nour. Feeding NMT a Healthy Diet -- The Impact of Quality, Quantity, or the Right Type of Nutrients. Proceedings of the 15th Biennial Conference of the ...

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A Comparison of Data Filtering Methods for Neural Machine Translation

Bane, Fred and Uguet, Celia Soler and Stribi \.z ew, Wiktor and Zaretskaya, Anna. A Comparison of Data Filtering Methods for Neural Machine Translation. Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and ...

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Machine Translate: Open resources and community

Yalangozian, Cecilia OL and Zouhar, Vil \'e m and Bittlingmayer, Adam. Machine Translate: Open resources and community. Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Tr...

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Unlocking the value of bilingual translated documents with Deep Learning Segmentation and Alignment for A rabic

Al-Khdour, Nour and Jonsson, Rebecca and Jaikat, Ruba W and Nasir, Abdallah and Alisis, Sara and Qardan, Sara and Shawahneh, Eyas. Unlocking the value of bilingual translated documents with Deep Learning Segmentation and Alignment for A rabic. Proceedings of the 15th Biennial ...

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Language I / O : Our Solution for Multilingual Customer Support

Bartolome, Diego and Dodel, Silke and Jacob, Chris. Language I / O : Our Solution for Multilingual Customer Support. Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track). 2022

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A Proposed User Study on MT -Enabled Scanning

Martindale, Marianna J and Carpuat, Marine. A Proposed User Study on MT -Enabled Scanning. Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track). 2022

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You ' ve translated it, now what?

Maxwell, Michael and Tafreshi, Shabnam and Richburg, Aquia and Kodali, Balaji and Brown, Kymani. You ' ve translated it, now what?. Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and G...

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SG Translate Together - Uplifting S ingapore ' s translation standards with the community through technology

Li, Lee Siew and Sim, Adeline and Kanagarajah, Gowri and Amirah, Siti and Xiang, Foo Yong and Ayathorai, Gayathri and Rasol, Sarina Mohamed and Ai Ti, Aw and Kui, Wu and Weihua, Zheng and Yang, Ding and Kumar Vangani, Tarun and Johan, Nabilah Binte Md. SG Translate Together - ...

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Multi-dimensional Consideration of Cognitive Effort in Translation and Interpreting Process Studies

Zou, Deyan. Multi-dimensional Consideration of Cognitive Effort in Translation and Interpreting Process Studies. Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track). 2022

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Thoughts on the History of Machine Translation in the U nited S tates

DeCamp, Jennifer A. Thoughts on the History of Machine Translation in the U nited S tates. Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track). 2022

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Hand in 01101000 01100001 01101110 01100100 with the Machine: A Roadmap to Quality

Mallette, Caroline-Soledad. Hand in 01101000 01100001 01101110 01100100 with the Machine: A Roadmap to Quality. Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track). 2022

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Robust Translation of F rench Live Speech Transcripts

Bertin-Lem \'e e, Elise and Klein, Guillaume and Crego, Josep and Senellart, Jean. Robust Translation of F rench Live Speech Transcripts. Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track...

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Speech-to-Text and Evaluation of Multiple Machine Translation Systems

Tzoukermann, Evelyne and Van Guilder, Steven and Doyon, Jennifer and Harke, Ekaterina. Speech-to-Text and Evaluation of Multiple Machine Translation Systems. Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users...

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Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track). 2022

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Building Machine Translation System for Software Product Descriptions Using Domain-specific Sub-corpora Extraction

Lohar, Pintu and Madden, Sinead and O ' Connor, Edmond and Popovic, Maja and Habruseva, Tanya. Building Machine Translation System for Software Product Descriptions Using Domain-specific Sub-corpora Extraction. Proceedings of the 15th biennial conference of the Association for...

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Domain-Specific Text Generation for Machine Translation

Moslem, Yasmin and Haque, Rejwanul and Kelleher, John and Way, Andy. Domain-Specific Text Generation for Machine Translation. Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track). 2022

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Strategies for Adapting Multilingual Pre-training for Domain-Specific Machine Translation

Verma, Neha and Murray, Kenton and Duh, Kevin. Strategies for Adapting Multilingual Pre-training for Domain-Specific Machine Translation. Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track). 2022

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Prefix Embeddings for In-context Machine Translation

Sia, Suzanna and Duh, Kevin. Prefix Embeddings for In-context Machine Translation. Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track). 2022

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Fast Vocabulary Projection Method via Clustering for Multilingual Machine Translation on GPU

Amer, Hossam and Afify, Mohamed and Kim, Young Jin and Matsushita, Hitokazu and Hassan, Hany. Fast Vocabulary Projection Method via Clustering for Multilingual Machine Translation on GPU. Proceedings of the 15th biennial conference of the Association for Machine Translation in...

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Language Tokens: A Frustratingly Simple Approach Improves Zero-Shot Performance of Multilingual Translation

ElNokrashy, Muhammad and Hendy, Amr and Maher, Mohamed and Afify, Mohamed and Hassan Awadalla, Hany. Language Tokens: A Frustratingly Simple Approach Improves Zero-Shot Performance of Multilingual Translation. Proceedings of the 15th biennial conference of the Association for ...

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Low Resource Chat Translation: A Benchmark for H indi -- E nglish Language Pair

Gain, Baban and Appicharla, Ramakrishna and Chennabasavaraj, Soumya and Garera, Nikesh and Ekbal, Asif and Chelliah, Muthusamy. Low Resource Chat Translation: A Benchmark for H indi -- E nglish Language Pair. Proceedings of the 15th biennial conference of the Association for M...

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How Robust is Neural Machine Translation to Language Imbalance in Multilingual Tokenizer Training?

Zhang, Shiyue and Chaudhary, Vishrav and Goyal, Naman and Cross, James and Wenzek, Guillaume and Bansal, Mohit and Guzman, Francisco. How Robust is Neural Machine Translation to Language Imbalance in Multilingual Tokenizer Training?. Proceedings of the 15th biennial conference...

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How Effective is Byte Pair Encoding for Out-Of-Vocabulary Words in Neural Machine Translation?

Araabi, Ali and Monz, Christof and Niculae, Vlad. How Effective is Byte Pair Encoding for Out-Of-Vocabulary Words in Neural Machine Translation?. Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track). 2022

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On the Effectiveness of Quasi Character-Level Models for Machine Translation

Carri \'o n, Salvador and Casacuberta, Francisco. On the Effectiveness of Quasi Character-Level Models for Machine Translation. Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track). 2022

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Improving Translation of Out Of Vocabulary Words using Bilingual Lexicon Induction in Low-Resource Machine Translation

Waldendorf, Jonas and Birch, Alexandra and Hadow, Barry and Micele Barone, Antonio Valerio. Improving Translation of Out Of Vocabulary Words using Bilingual Lexicon Induction in Low-Resource Machine Translation. Proceedings of the 15th biennial conference of the Association fo...

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Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation

Tan, Weiting and Ding, Shuoyang and Khayrallah, Huda and Koehn, Philipp. Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation. Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track). 2022

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Limitations and Challenges of Unsupervised Cross-lingual Pre-training

Quesada Zaragoza, Mart \'i n and Casacuberta, Francisco. Limitations and Challenges of Unsupervised Cross-lingual Pre-training. Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track). 2022

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Few-Shot Regularization to Tackle Catastrophic Forgetting in Multilingual Machine Translation

Carri \'o n, Salvador and Casacuberta, Francisco. Few-Shot Regularization to Tackle Catastrophic Forgetting in Multilingual Machine Translation. Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track). 2022

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Quantized W asserstein P rocrustes Alignment of Word Embedding Spaces

Aboagye, Prince O and Zheng, Yan and Yeh, Michael and Wang, Junpeng and Zhuang, Zhongfang and Chen, Huiyuan and Wang, Liang and Zhang, Wei and Phillips, Jeff. Quantized W asserstein P rocrustes Alignment of Word Embedding Spaces. Proceedings of the 15th biennial conference of ...

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Refining an Almost Clean Translation Memory Helps Machine Translation

Bhardwa, Shivendra and Alfonso-Hermelo, David and Langlais, Philippe and Bernier-Colborne, Gabriel and Goutte, Cyril and Simard, Michel. Refining an Almost Clean Translation Memory Helps Machine Translation. Proceedings of the 15th biennial conference of the Association for Ma...

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Practical Attacks on Machine Translation using Paraphrase

Merkhofer, Elizabeth M and Henderson, John and Gertner, Abigail and Doyle, Michael and Wong, Lily. Practical Attacks on Machine Translation using Paraphrase. Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Resea...

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Sign Language Machine Translation and the Sign Language Lexicon: A Linguistically Informed Approach

Murtagh, Irene and Nogales, V \'i ctor Ubieto and Blat, Josep. Sign Language Machine Translation and the Sign Language Lexicon: A Linguistically Informed Approach. Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1:...

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A Neural Machine Translation Approach to Translate Text to Pictographs in a Medical Speech Translation System - The B abel D r Use Case

Mutal, Jonathan and Bouillon, Pierrette and Norr \'e , Magali and Gerlach, Johanna and Ormaechea Grijalba, Luc \'i a. A Neural Machine Translation Approach to Translate Text to Pictographs in a Medical Speech Translation System - The B abel D r Use Case. Proceedings of the 15t...

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Embedding-Enhanced GIZA ++: Improving Low-Resource Word Alignment Using Embeddings

Marchisio, Kelly and Xiong, Conghao and Koehn, Philipp. Embedding-Enhanced GIZA ++: Improving Low-Resource Word Alignment Using Embeddings. Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track). 2022

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Gender bias Evaluation in L uganda- E nglish Machine Translation

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Adapting Large Multilingual Machine Translation Models to Unseen Low Resource Languages via Vocabulary Substitution and Neuron Selection

Abdelghaffar, Mohamed A and El Mogy, Amr and Sharaf, Nada Ahmed. Adapting Large Multilingual Machine Translation Models to Unseen Low Resource Languages via Vocabulary Substitution and Neuron Selection. Proceedings of the 15th biennial conference of the Association for Machine...

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Measuring the Effects of Human and Machine Translation on Website Engagement

Kovacs, Geza and DeNero, John. Measuring the Effects of Human and Machine Translation on Website Engagement. Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track). 2022

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Consistent Human Evaluation of Machine Translation across Language Pairs

Licht, Daniel and Gao, Cynthia and Lam, Janice and Guzman, Francisco and Diab, Mona and Koehn, Philipp. Consistent Human Evaluation of Machine Translation across Language Pairs. Proceedings of the 15th biennial conference of the Association for Machine Translation in the Ameri...

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Evaluating Machine Translation in Cross-lingual E -Commerce Search

Zhang, Hang and Tan, Liling and Misra, Amita. Evaluating Machine Translation in Cross-lingual E -Commerce Search. Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track). 2022

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Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Workshop 2: Corpus Generation and Corpus Augmentation for Machine Translation). 2022

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E nglish- R ussian Data Augmentation for Neural Machine Translation

Teslenko Grygoryev, Nikita and Garcia Martinez, Mercedes and Casacuberta Nolla, Francisco and Estela Pastor, Amando and Herranz, Manuel. E nglish- R ussian Data Augmentation for Neural Machine Translation. Proceedings of the 15th biennial conference of the Association for Mach...

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Efficient Machine Translation Corpus Generation

Yuksel, Kamer Ali and Gunduz, Ahmet and Sharma, Shreyas and Sawaf, Hassan. Efficient Machine Translation Corpus Generation. Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Workshop 2: Corpus Generation and Corpus Augmenta...

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Building and Analysis of T amil Lyric Corpus with Semantic Representation

Ranganathan, Karthika and T V, Geetha. Building and Analysis of T amil Lyric Corpus with Semantic Representation. Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Workshop 2: Corpus Generation and Corpus Augmentation for M...

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U krainian-To- E nglish Folktale Corpus: Parallel Corpus Creation and Augmentation for Machine Translation in Low-Resource Languages

Burda-Lassen, Olena. U krainian-To- E nglish Folktale Corpus: Parallel Corpus Creation and Augmentation for Machine Translation in Low-Resource Languages. Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Workshop 2: Corpus...

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Proceedings of the 20th Annual Workshop of the Australasian Language Technology Association. 2022

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The Corpus of A ustralian and N ew Z ealand Spoken E nglish: A new resource of naturalistic speech transcripts

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Using public domain resources and off-the-shelf tools to produce high-quality multimedia texts

Rayner, Manny and Chiera, Belinda and Chua, Cathy. Using public domain resources and off-the-shelf tools to produce high-quality multimedia texts. Proceedings of the 20th Annual Workshop of the Australasian Language Technology Association. 2022

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The Role of Context in Vaccine Stance Prediction for T witter Users

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TCG -Event: Effective Task Conditioning for Generation-based Event Extraction

Shiri, Fatemeh and Wu, Tongtong and Li, Yuanfang and Haffari, Gholamreza. TCG -Event: Effective Task Conditioning for Generation-based Event Extraction. Proceedings of the 20th Annual Workshop of the Australasian Language Technology Association. 2022

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Complex Reading Comprehension Through Question Decomposition

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Using Aspect-Based Sentiment Analysis to Classify Attitude-bearing Words

Parameswaran, Pradeesh and Trotman, Andrew and Liesaputra, Veronica and Eyers, David. Using Aspect-Based Sentiment Analysis to Classify Attitude-bearing Words. Proceedings of the 20th Annual Workshop of the Australasian Language Technology Association. 2022

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Fine-tuning a Subtle Parsing Distinction Using a Probabilistic Decision Tree: the Case of Postnominal ``that'' in Noun Complement Clauses vs

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Lim, Ho Hung and Cai, Tianyuan and Lee, John S. Y. and Liu, Meichun. Robustness of Hybrid Models in Cross-domain Readability Assessment. Proceedings of the 20th Annual Workshop of the Australasian Language Technology Association. 2022

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Specifying Optimisation Problems for Declarative Programs in Precise Natural Language

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Improving Text-based Early Prediction by Distillation from Privileged Time-Series Text

Liu, Jinghui and Capurro, Daniel and Nguyen, Anthony and Verspoor, Karin. Improving Text-based Early Prediction by Distillation from Privileged Time-Series Text. Proceedings of the 20th Annual Workshop of the Australasian Language Technology Association. 2022

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A D istil BERT opic Model for Short Text Documents

Rashid, Junaid and Kim, Jungeun and Naseem, Usman and Hussain, Amir. A D istil BERT opic Model for Short Text Documents. Proceedings of the 20th Annual Workshop of the Australasian Language Technology Association. 2022

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Generating Code-Switched Text from Monolingual Text with Dependency Tree

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Stability of Forensic Text Comparison System

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Academic Curriculum Generation using W ikipedia for External Knowledge

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Interactive Rationale Extraction for Text Classification

Dai, Jiayi and Kim, Mi-Young and Goebel, Randy. Interactive Rationale Extraction for Text Classification. Proceedings of the 20th Annual Workshop of the Australasian Language Technology Association. 2022

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Automatic Explanation Generation For Climate Science Claims

Xing, Rui and Bhatia, Shraey and Baldwin, Timothy and Lau, Jey Han. Automatic Explanation Generation For Climate Science Claims. Proceedings of the 20th Annual Workshop of the Australasian Language Technology Association. 2022

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Zhangzhou Implosives and Their Variations

Huang, Yishan and Hyslop, Gwendolyn. Zhangzhou Implosives and Their Variations. Proceedings of the 20th Annual Workshop of the Australasian Language Technology Association. 2022

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Evaluating the Examiner: The Perils of P earson Correlation for Validating Text Similarity Metrics

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Can Language Models Help in System Security? Investigating Log Anomaly Detection using BERT

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A Semantics of Spatial Expressions for interacting with unmanned aerial vehicles

Domingos, Lucas and Santos, Paulo. A Semantics of Spatial Expressions for interacting with unmanned aerial vehicles. Proceedings of the 20th Annual Workshop of the Australasian Language Technology Association. 2022

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Akram and Chy, Abu Nowshed

Aziz, Abdul and Hossain, Md. Akram and Chy, Abu Nowshed. Enhancing the D e BERT a Transformers Model for Classifying Sentences from Biomedical Abstracts. Proceedings of the 20th Annual Workshop of the Australasian Language Technology Association. 2022

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Textstar: a Fast and Lightweight Graph-Based Algorithm for Extractive Summarization and Keyphrase Extraction

Brock, David and Khan, Ali and Doan, Tam and Lin, Alicia and Guo, Yifan and Tarau, Paul. Textstar: a Fast and Lightweight Graph-Based Algorithm for Extractive Summarization and Keyphrase Extraction. Proceedings of the 20th Annual Workshop of the Australasian Language Technolog...

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Contrastive Visual and Language Learning for Visual Relationship Detection

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Overview of the 2022 ALTA Shared task: PIBOSO sentence classification, 10 years later

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Automatic Classification of Evidence Based Medicine Using Transformers

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Context-Aware Sentence Classification in Evidence-Based Medicine

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A Gentle Introduction to Deep Nets and Opportunities for the Future

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ACL Tutorial Proposal: Towards Reproducible Machine Learning Research in Natural Language Processing

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Knowledge-Augmented Methods for Natural Language Processing

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Non-Autoregressive Sequence Generation

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RFBFN : A Relation-First Blank Filling Network for Joint Relational Triple Extraction

Li, Zhe and Fu, Luoyi and Wang, Xinbing and Zhang, Haisong and Zhou, Chenghu. RFBFN : A Relation-First Blank Filling Network for Joint Relational Triple Extraction. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Worksh...

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Building a Dialogue Corpus Annotated with Expressed and Experienced Emotions

Ide, Tatsuya and Kawahara, Daisuke. Building a Dialogue Corpus Annotated with Expressed and Experienced Emotions. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022. doi:10.18653/v1/2022.acl-srw.3

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Darkness can not drive out darkness: Investigating Bias in Hate S peech D etection Models

Elsafoury, Fatma. Darkness can not drive out darkness: Investigating Bias in Hate S peech D etection Models. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022

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[234]

Ethical Considerations for Low-resourced Machine Translation

Haroutunian, Levon. Ethical Considerations for Low-resourced Machine Translation. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022. doi:10.18653/v1/2022.acl-srw.5

2022 doi
[235]

Integrating Question Rewrites in Conversational Question Answering: A Reinforcement Learning Approach

Ishii, Etsuko and Wilie, Bryan and Xu, Yan and Cahyawijaya, Samuel and Fung, Pascale. Integrating Question Rewrites in Conversational Question Answering: A Reinforcement Learning Approach. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics:...

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What Do You Mean by Relation Extraction? A Survey on Datasets and Study on Scientific Relation Classification

Bassignana, Elisa and Plank, Barbara. What Do You Mean by Relation Extraction? A Survey on Datasets and Study on Scientific Relation Classification. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022. doi:10...

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Logical Inference for Counting on Semi-structured Tables

Kurosawa, Tomoya and Yanaka, Hitomi. Logical Inference for Counting on Semi-structured Tables. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022. doi:10.18653/v1/2022.acl-srw.8

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[238]

GNN er: Reducing Overlapping in Span-based NER Using Graph Neural Networks

Zaratiana, Urchade and Tomeh, Nadi and Holat, Pierre and Charnois, Thierry. GNN er: Reducing Overlapping in Span-based NER Using Graph Neural Networks. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022. doi...

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[239]

Compositional Semantics and Inference System for Temporal Order based on J apanese CCG

Sugimoto, Tomoki and Yanaka, Hitomi. Compositional Semantics and Inference System for Temporal Order based on J apanese CCG. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022. doi:10.18653/v1/2022.acl-srw.10

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Combine to Describe: Evaluating Compositional Generalization in Image Captioning

Pantazopoulos, Georgios and Suglia, Alessandro and Eshghi, Arash. Combine to Describe: Evaluating Compositional Generalization in Image Captioning. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022. doi:10....

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Towards Unification of Discourse Annotation Frameworks

Fu, Yingxue. Towards Unification of Discourse Annotation Frameworks. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022. doi:10.18653/v1/2022.acl-srw.12

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[242]

Elif and Eryi

Oral, K. Elif and Eryi. AMR Alignment for Morphologically-rich and Pro-drop Languages. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022. doi:10.18653/v1/2022.acl-srw.13

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[243]

Sketching a Linguistically-Driven Reasoning Dialog Model for Social Talk

Lưu, Alex. Sketching a Linguistically-Driven Reasoning Dialog Model for Social Talk. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022. doi:10.18653/v1/2022.acl-srw.14

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Scoping natural language processing in I ndonesian and M alay for education applications

Maxwell-Smith, Zara and Kohler, Michelle and Suominen, Hanna. Scoping natural language processing in I ndonesian and M alay for education applications. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022. doi...

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[245]

E nglish- M alay Cross-Lingual Embedding Alignment using Bilingual Lexicon Augmentation

Lim, Ying Hao and Liew, Jasy Suet Yan. E nglish- M alay Cross-Lingual Embedding Alignment using Bilingual Lexicon Augmentation. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022. doi:10.18653/v1/2022.acl-srw.16

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[246]

Towards Detecting Political Bias in H indi News Articles

Agrawal, Samyak and Gupta, Kshitij and Gautam, Devansh and Mamidi, Radhika. Towards Detecting Political Bias in H indi News Articles. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022. doi:10.18653/v1/2022....

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Restricted or Not: A General Training Framework for Neural Machine Translation

Li, Zuchao and Utiyama, Masao and Sumita, Eiichiro and Zhao, Hai. Restricted or Not: A General Training Framework for Neural Machine Translation. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022. doi:10.18...

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What do Models Learn From Training on More Than Text? Measuring Visual Commonsense Knowledge

Hagstr. What do Models Learn From Training on More Than Text? Measuring Visual Commonsense Knowledge. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022. doi:10.18653/v1/2022.acl-srw.19

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[249]

T elugu NER : Leveraging Multi-Domain Named Entity Recognition with Deep Transformers

Duggenpudi, Suma Reddy and Oota, Subba Reddy and Marreddy, Mounika and Mamidi, Radhika. T elugu NER : Leveraging Multi-Domain Named Entity Recognition with Deep Transformers. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Resea...

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[250]

Using Neural Machine Translation Methods for Sign Language Translation

Angelova, Galina and Avramidis, Eleftherios and M. Using Neural Machine Translation Methods for Sign Language Translation. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022. doi:10.18653/v1/2022.acl-srw.21

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Flexible Visual Grounding

Kim, Yongmin and Chu, Chenhui and Kurohashi, Sadao. Flexible Visual Grounding. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022. doi:10.18653/v1/2022.acl-srw.22

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[252]

A large-scale computational study of content preservation measures for text style transfer and paraphrase generation

Babakov, Nikolay and Dale, David and Logacheva, Varvara and Panchenko, Alexander. A large-scale computational study of content preservation measures for text style transfer and paraphrase generation. Proceedings of the 60th Annual Meeting of the Association for Computational L...

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[253]

Explicit Object Relation Alignment for Vision and Language Navigation

Zhang, Yue and Kordjamshidi, Parisa. Explicit Object Relation Alignment for Vision and Language Navigation. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022. doi:10.18653/v1/2022.acl-srw.24

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[254]

Mining Logical Event Schemas From Pre-Trained Language Models

Lawley, Lane and Schubert, Lenhart. Mining Logical Event Schemas From Pre-Trained Language Models. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022. doi:10.18653/v1/2022.acl-srw.25

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[255]

Exploring Cross-lingual Text Detoxification with Large Multilingual Language Models

Moskovskiy, Daniil and Dementieva, Daryna and Panchenko, Alexander. Exploring Cross-lingual Text Detoxification with Large Multilingual Language Models. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022. do...

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[256]

MEKER : Memory Efficient Knowledge Embedding Representation for Link Prediction and Question Answering

Chekalina, Viktoriia and Razzhigaev, Anton and Sayapin, Albert and Frolov, Evgeny and Panchenko, Alexander. MEKER : Memory Efficient Knowledge Embedding Representation for Link Prediction and Question Answering. Proceedings of the 60th Annual Meeting of the Association for Com...

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[257]

Discourse on ASR Measurement: Introducing the ARPOCA Assessment Tool

Merz, Megan and Scrivner, Olga. Discourse on ASR Measurement: Introducing the ARPOCA Assessment Tool. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022. doi:10.18653/v1/2022.acl-srw.28

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[258]

Pretrained Knowledge Base Embeddings for improved Sentential Relation Extraction

Papaluca, Andrea and Krefl, Daniel and Suominen, Hanna and Lenskiy, Artem. Pretrained Knowledge Base Embeddings for improved Sentential Relation Extraction. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022...

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[259]

Improving Cross-domain, Cross-lingual and Multi-modal Deception Detection

Panda, Subhadarshi and Levitan, Sarah Ita. Improving Cross-domain, Cross-lingual and Multi-modal Deception Detection. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022. doi:10.18653/v1/2022.acl-srw.30

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[260]

Automatic Generation of Distractors for Fill-in-the-Blank Exercises with Round-Trip Neural Machine Translation

Panda, Subhadarshi and Palma Gomez, Frank and Flor, Michael and Rozovskaya, Alla. Automatic Generation of Distractors for Fill-in-the-Blank Exercises with Round-Trip Neural Machine Translation. Proceedings of the 60th Annual Meeting of the Association for Computational Linguis...

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[261]

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Alastruey, Belen and Ferrando, Javier and G \'a llego, Gerard I. and Costa-juss \`a , Marta R. On the Locality of Attention in Direct Speech Translation. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022. d...

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[262]

Extraction of Diagnostic Reasoning Relations for Clinical Knowledge Graphs

Socrates, Vimig. Extraction of Diagnostic Reasoning Relations for Clinical Knowledge Graphs. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022. doi:10.18653/v1/2022.acl-srw.33

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[263]

Scene-Text Aware Image and Text Retrieval with Dual-Encoder

Miyawaki, Shumpei and Hasegawa, Taku and Nishida, Kyosuke and Kato, Takuma and Suzuki, Jun. Scene-Text Aware Image and Text Retrieval with Dual-Encoder. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022. do...

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Towards Fine-grained Classification of Climate Change related Social Media Text

Vaid, Roopal and Pant, Kartikey and Shrivastava, Manish. Towards Fine-grained Classification of Climate Change related Social Media Text. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022. doi:10.18653/v1/2...

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[265]

Deep Neural Representations for Multiword Expressions Detection

Kanclerz, Kamil and Piasecki, Maciej. Deep Neural Representations for Multiword Expressions Detection. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022. doi:10.18653/v1/2022.acl-srw.36

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[266]

A Checkpoint on Multilingual Misogyny Identification

Muti, Arianna and Barr \'o n-Cede \ n o, Alberto. A Checkpoint on Multilingual Misogyny Identification. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022. doi:10.18653/v1/2022.acl-srw.37

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[267]

Using dependency parsing for few-shot learning in distributional semantics

Preda, Stefania and Emerson, Guy. Using dependency parsing for few-shot learning in distributional semantics. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022. doi:10.18653/v1/2022.acl-srw.38

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[268]

A Dataset and BERT -based Models for Targeted Sentiment Analysis on T urkish Texts

Mutlu, Mustafa Melih and. A Dataset and BERT -based Models for Targeted Sentiment Analysis on T urkish Texts. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2022. doi:10.18653/v1/2022.acl-srw.39

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[269]

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2022

2022
[270]

B it F it: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models

Ben Zaken, Elad and Goldberg, Yoav and Ravfogel, Shauli. B it F it: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2022. doi:...

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Are Shortest Rationales the Best Explanations for Human Understanding?

Shen, Hua and Wu, Tongshuang and Guo, Wenbo and Huang, Ting-Hao. Are Shortest Rationales the Best Explanations for Human Understanding?. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2022. doi:10.18653/v1/2022...

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[272]

Analyzing Wrap-Up Effects through an Information-Theoretic Lens

Meister, Clara and Pimentel, Tiago and Clark, Thomas and Cotterell, Ryan and Levy, Roger. Analyzing Wrap-Up Effects through an Information-Theoretic Lens. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2022. do...

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[273]

Have my arguments been replied to? Argument Pair Extraction as Machine Reading Comprehension

Bao, Jianzhu and Sun, Jingyi and Zhu, Qinglin and Xu, Ruifeng. Have my arguments been replied to? Argument Pair Extraction as Machine Reading Comprehension. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2022. ...

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[274]

On the probability -- quality paradox in language generation

Meister, Clara and Wiher, Gian and Pimentel, Tiago and Cotterell, Ryan. On the probability -- quality paradox in language generation. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2022. doi:10.18653/v1/2022.ac...

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[275]

Disentangled Knowledge Transfer for OOD Intent Discovery with Unified Contrastive Learning

Mou, Yutao and He, Keqing and Wu, Yanan and Zeng, Zhiyuan and Xu, Hong and Jiang, Huixing and Wu, Wei and Xu, Weiran. Disentangled Knowledge Transfer for OOD Intent Discovery with Unified Contrastive Learning. Proceedings of the 60th Annual Meeting of the Association for Compu...

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[276]

Voxel-informed Language Grounding

Corona, Rodolfo and Zhu, Shizhan and Klein, Dan and Darrell, Trevor. Voxel-informed Language Grounding. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2022. doi:10.18653/v1/2022.acl-short.7

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[277]

P -Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks

Liu, Xiao and Ji, Kaixuan and Fu, Yicheng and Tam, Weng and Du, Zhengxiao and Yang, Zhilin and Tang, Jie. P -Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics ...

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[278]

On Efficiently Acquiring Annotations for Multilingual Models

Moniz, Joel Ruben Antony and Patra, Barun and Gormley, Matthew. On Efficiently Acquiring Annotations for Multilingual Models. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2022. doi:10.18653/v1/2022.acl-short.9

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[279]

Automatic Detection of Entity-Manipulated Text using Factual Knowledge

Jawahar, Ganesh and Abdul-Mageed, Muhammad and Lakshmanan, Laks. Automatic Detection of Entity-Manipulated Text using Factual Knowledge. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2022. doi:10.18653/v1/2022...

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[280]

Does BERT Know that the IS -A Relation Is Transitive?

Lin, Ruixi and Ng, Hwee Tou. Does BERT Know that the IS -A Relation Is Transitive?. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2022. doi:10.18653/v1/2022.acl-short.11

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[281]

Buy Tesla, Sell Ford: Assessing Implicit Stock Market Preference in Pre-trained Language Models

Chuang, Chengyu and Yang, Yi. Buy Tesla, Sell Ford: Assessing Implicit Stock Market Preference in Pre-trained Language Models. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2022. doi:10.18653/v1/2022.acl-short.12

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[282]

Pixie: Preference in Implicit and Explicit Comparisons

Haque, Amanul and Garg, Vaibhav and Guo, Hui and Singh, Munindar. Pixie: Preference in Implicit and Explicit Comparisons. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2022. doi:10.18653/v1/2022.acl-short.13

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[283]

Counterfactual Explanations for Natural Language Interfaces

Tolkachev, George and Mell, Stephen and Zdancewic, Stephan and Bastani, Osbert. Counterfactual Explanations for Natural Language Interfaces. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2022. doi:10.18653/v1/...

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Predicting Difficulty and Discrimination of Natural Language Questions

Byrd, Matthew and Srivastava, Shashank. Predicting Difficulty and Discrimination of Natural Language Questions. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2022. doi:10.18653/v1/2022.acl-short.15

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How does the pre-training objective affect what large language models learn about linguistic properties?

Alajrami, Ahmed and Aletras, Nikolaos. How does the pre-training objective affect what large language models learn about linguistic properties?. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2022. doi:10.18653...

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The Power of Prompt Tuning for Low-Resource Semantic Parsing

Schucher, Nathan and Reddy, Siva and de Vries, Harm. The Power of Prompt Tuning for Low-Resource Semantic Parsing. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2022. doi:10.18653/v1/2022.acl-short.17

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[287]

Data Contamination: From Memorization to Exploitation

Magar, Inbal and Schwartz, Roy. Data Contamination: From Memorization to Exploitation. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2022. doi:10.18653/v1/2022.acl-short.18

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Detecting Annotation Errors in Morphological Data with the Transformer

Liu, Ling and Hulden, Mans. Detecting Annotation Errors in Morphological Data with the Transformer. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2022. doi:10.18653/v1/2022.acl-short.19

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Estimating the Entropy of Linguistic Distributions

Arora, Aryaman and Meister, Clara and Cotterell, Ryan. Estimating the Entropy of Linguistic Distributions. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2022. doi:10.18653/v1/2022.acl-short.20

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Morphological Reinflection with Multiple Arguments: An Extended Annotation schema and a G eorgian Case Study

Guriel, David and Goldman, Omer and Tsarfaty, Reut. Morphological Reinflection with Multiple Arguments: An Extended Annotation schema and a G eorgian Case Study. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2...

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DQ - BART : Efficient Sequence-to-Sequence Model via Joint Distillation and Quantization

Li, Zheng and Wang, Zijian and Tan, Ming and Nallapati, Ramesh and Bhatia, Parminder and Arnold, Andrew and Xiang, Bing and Roth, Dan. DQ - BART : Efficient Sequence-to-Sequence Model via Joint Distillation and Quantization. Proceedings of the 60th Annual Meeting of the Associ...

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Learning-by-Narrating: Narrative Pre-Training for Zero-Shot Dialogue Comprehension

Zhao, Chao and Yao, Wenlin and Yu, Dian and Song, Kaiqiang and Yu, Dong and Chen, Jianshu. Learning-by-Narrating: Narrative Pre-Training for Zero-Shot Dialogue Comprehension. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Sho...

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Kronecker Decomposition for GPT Compression

Edalati, Ali and Tahaei, Marzieh and Rashid, Ahmad and Nia, Vahid and Clark, James and Rezagholizadeh, Mehdi. Kronecker Decomposition for GPT Compression. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2022. do...

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Simple and Effective Knowledge-Driven Query Expansion for QA -Based Product Attribute Extraction

Shinzato, Keiji and Yoshinaga, Naoki and Xia, Yandi and Chen, Wei-Te. Simple and Effective Knowledge-Driven Query Expansion for QA -Based Product Attribute Extraction. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Pape...

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Event-Event Relation Extraction using Probabilistic Box Embedding

Hwang, EunJeong and Lee, Jay-Yoon and Yang, Tianyi and Patel, Dhruvesh and Zhang, Dongxu and McCallum, Andrew. Event-Event Relation Extraction using Probabilistic Box Embedding. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: ...

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Sample, Translate, Recombine: Leveraging Audio Alignments for Data Augmentation in End-to-end Speech Translation

Lam, Tsz Kin and Schamoni, Shigehiko and Riezler, Stefan. Sample, Translate, Recombine: Leveraging Audio Alignments for Data Augmentation in End-to-end Speech Translation. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short ...

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Predicting Sentence Deletions for Text Simplification Using a Functional Discourse Structure

Zhang, Bohan and Choubey, Prafulla Kumar and Huang, Ruihong. Predicting Sentence Deletions for Text Simplification Using a Functional Discourse Structure. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2022. do...

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Multilingual Pre-training with Language and Task Adaptation for Multilingual Text Style Transfer

Lai, Huiyuan and Toral, Antonio and Nissim, Malvina. Multilingual Pre-training with Language and Task Adaptation for Multilingual Text Style Transfer. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2022. doi:10...

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When to Use Multi-Task Learning vs Intermediate Fine-Tuning for Pre-Trained Encoder Transfer Learning

Weller, Orion and Seppi, Kevin and Gardner, Matt. When to Use Multi-Task Learning vs Intermediate Fine-Tuning for Pre-Trained Encoder Transfer Learning. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2022. doi:...

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Leveraging Explicit Lexico-logical Alignments in Text-to- SQL Parsing

Sun, Runxin and He, Shizhu and Zhu, Chong and He, Yaohan and Li, Jinlong and Zhao, Jun and Liu, Kang. Leveraging Explicit Lexico-logical Alignments in Text-to- SQL Parsing. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short...

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Pith tools

Reviewed June 26, 2026 · model on record in the stance chip above.

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Proceedings of the 21st Workshop on Biomedical Language Processing. 2022

2022

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Explainable Assessment of Healthcare Articles with QA

Boissonnet, Alodie and Saeidi, Marzieh and Plachouras, Vassilis and Vlachos, Andreas. Explainable Assessment of Healthcare Articles with QA. Proceedings of the 21st Workshop on Biomedical Language Processing. 2022. doi:10.18653/v1/2022.bionlp-1.1

work page doi:10.18653/v1/2022.bionlp-1.1 2022

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A sequence-to-sequence approach for document-level relation extraction

Giorgi, John and Bader, Gary and Wang, Bo. A sequence-to-sequence approach for document-level relation extraction. Proceedings of the 21st Workshop on Biomedical Language Processing. 2022. doi:10.18653/v1/2022.bionlp-1.2

work page doi:10.18653/v1/2022.bionlp-1.2 2022

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Position-based Prompting for Health Outcome Generation

Abaho, Micheal and Bollegala, Danushka and Williamson, Paula and Dodd, Susanna. Position-based Prompting for Health Outcome Generation. Proceedings of the 21st Workshop on Biomedical Language Processing. 2022. doi:10.18653/v1/2022.bionlp-1.3

work page doi:10.18653/v1/2022.bionlp-1.3 2022

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How You Say It Matters: Measuring the Impact of Verbal Disfluency Tags on Automated Dementia Detection

Farzana, Shahla and Deshpande, Ashwin and Parde, Natalie. How You Say It Matters: Measuring the Impact of Verbal Disfluency Tags on Automated Dementia Detection. Proceedings of the 21st Workshop on Biomedical Language Processing. 2022. doi:10.18653/v1/2022.bionlp-1.4

work page doi:10.18653/v1/2022.bionlp-1.4 2022

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Zero-Shot Aspect-Based Scientific Document Summarization using Self-Supervised Pre-training

Soleimani, Amir and Nikoulina, Vassilina and Favre, Benoit and Ait Mokhtar, Salah. Zero-Shot Aspect-Based Scientific Document Summarization using Self-Supervised Pre-training. Proceedings of the 21st Workshop on Biomedical Language Processing. 2022. doi:10.18653/v1/2022.bionlp-1.5

work page doi:10.18653/v1/2022.bionlp-1.5 2022

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Data Augmentation for Biomedical Factoid Question Answering

Pappas, Dimitris and Malakasiotis, Prodromos and Androutsopoulos, Ion. Data Augmentation for Biomedical Factoid Question Answering. Proceedings of the 21st Workshop on Biomedical Language Processing. 2022. doi:10.18653/v1/2022.bionlp-1.6

work page doi:10.18653/v1/2022.bionlp-1.6 2022

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Slot Filling for Biomedical Information Extraction

Papanikolaou, Yannis and Staib, Marlene and Grace, Justin Joshua and Bennett, Francine. Slot Filling for Biomedical Information Extraction. Proceedings of the 21st Workshop on Biomedical Language Processing. 2022. doi:10.18653/v1/2022.bionlp-1.7

work page doi:10.18653/v1/2022.bionlp-1.7 2022