APE: Argument Pair Extraction from Peer Review and Rebuttal via Multi-task Learning
Open accessAbstract
Peer review and rebuttal, with rich interactions and argumentative discussions in between, are naturally a good resource to mine arguments.However, few works study both of them simultaneously.In this paper, we introduce a new argument pair extraction (APE) task on peer review and rebuttal in order to study the contents, the structure and the connections between them.We prepare a challenging dataset that contains 4,764 fully annotated review-rebuttal passage pairs from an open review platform to facilitate the study of this task.To automatically detect argumentative propositions and extract argument pairs from this corpus, we cast it as the combination of a sequence labeling task and a text relation classification task.Thus, we propose a multitask learning framework based on hierarchical LSTM networks.Extensive experiments and analysis demonstrate the effectiveness of our multi-task framework, and also show the challenges of the new task as well as motivate future research directions. 1
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Reviewer comments often initiate exchanges in which authors clarify evidence, defend methodological choices, or revise claims.
“Work on review-rebuttal interaction shows that reviewer comments often initiate exchanges in which authors clarify evidence, defend methodological choices, or revise claims (Cheng et al., 2020;D'Arcy et al., 2024).”
From Beyond Human-Likeness: Mapping the Scientific Critique Profiles of LLMs and Human Reviewers (2026) · cites this paper as Cheng et al., 2020
The Rebuttal example (page unavailable) shows authors answering a methodological question and agreeing to remove an overstated claim. Data Analysis (page unavailable) says authors reply to reviewers’ arguments by explaining their points clearly. The copy does not specifically show authors clarifying evidence, so that part of the attributed claim is not established. (LLM, conf=0.82)
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Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533 · see citing works
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
Lafferty, J., McCallum, A., & Pereira, F. C. N. (2001) · ScholarlyCommons (University of Pennsylvania) · Cited by 12,953 · see citing works
Neural Architectures for Named Entity Recognition
Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., & Dyer, C. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/n16-1030 · Cited by 4,401 · see citing works
Text Chunking using Transformation-Based Learning
Ramshaw, L., & Marcus, M. P. (1995) · VLC@ACL · doi:10.1007/978-94-017-2390-9_10 · Cited by 2,031 · see citing works
Named Entity Recognition with Bidirectional LSTM-CNNs
Chiu, J., & Nichols, E. (2016) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00104 · Cited by 1,925 · see citing works
Design Challenges and Misconceptions in Named Entity Recognition
Ratinov, L., & Roth, D. (2009) · Conference on Computational Natural Language Learning · doi:10.3115/1596374.1596399 · Cited by 1,640 · see citing works
Lawrence, J. S., & Reed, C. (2019) · Computational Linguistics · doi:10.1162/coli_a_00364 · Cited by 430 · see citing works
Peer Review in Scientific Publications: Benefits, Critiques, & A Survival Guide.
Kelly, J., Sadeghieh, T., & Adeli, K. (2014) · PubMed Central · Cited by 391 · see citing works
Identifying Argumentative Discourse Structures in Persuasive Essays
Stab, C., & Gurevych, I. (2014) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1006 · Cited by 373 · see citing works
Palau, R. M., & Moens, M. (2011) · Artificial Intelligence and Law · doi:10.1007/s10506-010-9104-x · Cited by 261 · see citing works
A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications
Kang, D., Ammar, W., Dalvi, B., Zuylen, M., Kohlmeier, S., Hovy, E., & Schwartz, R. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1149 · Cited by 171 · see citing works
End-to-End Argumentation Mining in Student Essays
Persing, I., & Ng, V. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1164 · Cited by 118 · see citing works
Argument Mining: Extracting Arguments from Online Dialogue
Swanson, R., Ecker, B., & Walker, M. A. (2015) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/w15-4631 · Cited by 113 · see citing works
AMPERSAND: Argument Mining for PERSuAsive oNline Discussions
Chakrabarty, T., Hidey, C., Muresan, S., McKeown, K., & Hwang, A. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1291 · Cited by 99 · see citing works
Argument Mining for Understanding Peer Reviews
Hua, X., Nikolov, M., Badugu, N., & Wang, L. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1219 · Cited by 86 · cited 2× · see citing works
“As the number of sub-missions increases rapidly in recent years, how to evaluate the submissions effectively and efficiently becomes an attention-attracting challenge, and the study on peer review itself emerges as a new research topic (Xiong and Litman, 2011; Hua et al., 2019b).”
BackgroundAutomatic Argument Quality Assessment - New Datasets and Methods
Toledo, A., Gretz, S., Cohen-Karlik, E., Friedman, R., Venezian, E., Lahav, D., Jacovi, M., Aharonov, R., & Slonim, N. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1564 · Cited by 67 · see citing works
Will it Blend? Blending Weak and Strong Labeled Data in a Neural Network for Argumentation Mining
Shnarch, E., Alzate, C., Dankin, L., Gleize, M., Hou, Y., Choshen, L., Aharonov, R., & Slonim, N. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-2095 · Cited by 67 · see citing works
Internet Argument Corpus 2.0: An SQL schema for Dialogic Social Media and the Corpora to go with it
Abbott, R., Ecker, B., Anand, P., & Walker, M. A. (2016) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2bojywdmep7s · Cited by 65 · see citing works
Argumentation Quality Assessment: Theory vs. Practice
Wachsmuth, H., Naderi, N., Habernal, I., Hou, Y., Hirst, G., Gurevych, I., & Stein, B. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-2039 · Cited by 63 · see citing works
Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network
Gleize, M., Shnarch, E., Choshen, L., Dankin, L., Moshkowich, G., Aharonov, R., & Slonim, N. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1093 · Cited by 62 · see citing works
Automatically Predicting Peer-Review Helpfulness
Xiong, W., & Litman, D. J. (2011) · D-Scholarship@Pitt (University of Pittsburgh) · Cited by 59 · see citing works
Identifying Prominent Arguments in Online Debates Using Semantic Textual Similarity
Boltužić, F., & Šnajder, J. (2015) · doi:10.3115/v1/w15-0514 · Cited by 59 · see citing works
Neural Argument Generation Augmented with Externally Retrieved Evidence
Hua, X., & Wang, L. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1021 · Cited by 56 · see citing works
Argument Generation with Retrieval, Planning, and Realization
Hua, X., Hu, Z., & Wang, L. (2019) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.18653/v1/p19-1255 · Cited by 55 · see citing works
Aspect-Controlled Neural Argument Generation
Schiller, B., Daxenberger, J., & Gurevych, I. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.34 · Cited by 55 · see citing works
Does My Rebuttal Matter? Insights from a Major NLP Conference
Gao, Y., Eger, S., Kuznetsov, I., Gurevych, I., & Miyao, Y. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1129 · Cited by 48 · see citing works
Reviewing Reviews: An Evaluation of Peer Reviews of Journal Article Submissions
Falkenberg, L. J., & Soranno, P. A. (2018) · Limnology and Oceanography Bulletin · doi:10.1002/lob.10217 · Cited by 38 · see citing works
A Large-Scale Dataset for Argument Quality Ranking: Construction and Analysis
Gretz, S., Friedman, R., Cohen-Karlik, E., Toledo, A., Lahav, D., Aharonov, R., & Slonim, N. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6285 · Cited by 23 · see citing works
Argument Relation Classification Using a Joint Inference Model
Hou, Y., & Jochim, C. (2017) · Workshop on Argument Mining (ArgMining) · doi:10.18653/v1/w17-5107 · Cited by 23 · see citing works
Cross-Lingual Argumentative Relation Identification: from English to Portuguese
Rocha, G., Stab, C., Cardoso, H. L., & Gurevych, I. (2018) · ArgMining@EMNLP · doi:10.18653/v1/w18-5217 · Cited by 19 · see citing works
Does My Rebuttal Matter? Insights from a Major NLP Conference
Gao, Y., Eger, S., Kuznetsov, I., Gurevych, I., & Miyao, Y. (2019) · TUbilio (Technical University of Darmstadt) · doi:10.48550/arxiv.1903.11367 · Cited by 16 · see citing works
A Machine Learning Approach to Argument Mining in Legal Documents
Poudyal, P. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00178-0_30 · Cited by 9 · see citing works
Fine-Grained Argument Unit Recognition and Classification
Trautmann, D., Daxenberger, J., Stab, C., Schütze, H., & Gurevych, I. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6438 · Cited by 4 · see citing works
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805
Flagged first, then by citations
1 of the 38 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Cor- rado, and Jeff Dean. 2013. Distributed representa- tions of words and phrases and their compositional- ity. In Proceedings of NIPS.
- Gil Rocha, Christian Stab, Henrique Lopes Cardoso, and Iryna Gurevych. 2018. Cross-lingual argumen- tative relation identification: from english to por- tuguese. In Proceedings of the 5th Workshop on Ar- gument Mining.
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IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks
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He, N., Chen, Q., Yu, Q., & Han, Z. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44223-0_9 · Cited by 1 · see citing works
Self-citationYu, N., Liu, J., & Shi, Y. (2024) · IEEE Signal Processing Letters · doi:10.1109/lsp.2024.3412531 · Cited by 1 · see citing works
Beyond Human-Likeness: Mapping the Scientific Critique Profiles of LLMs and Human Reviewers
Yang, Y., Thelwall, M., & He, G. (2026) · arXiv · doi:10.48550/arxiv.2609.01895
MReD: A Meta-Review Dataset for Controllable Text Generation.Preprint
Shen, C., Cheng, L., Zhou, R., Bing, L., You, Y., & Si, L. (2021) · arXiv (Cornell University)
Self-citationA Dataset for Discourse Structure in Peer Review Discussions.Preprint
Kennard, N. N., O’Gorman, T., Sharma, A., Bagchi, C., Clinton, M., Yelugam, P. K., Das, R., Zamani, H., & McCallum, A. (2021) · arXiv (Cornell University)
How to Use Theory to Implement Natural Language Processing for Peer-Feedback
Greisel, M., Bauer, E., Kuznetsov, I., Berndt, M., Dresel, M., Fischer, M. R., Kollar, I., & Fischer, F. (2023) · Computer-supported collaborative learning/The Computer-Supported Collaborative Learning Conference · doi:10.22318/cscl2023.108887
Mao, T., Fu, J., & Yoshie, O. (2024) · Asia Conference on Information Engineering (ACIE) · doi:10.1109/acie61839.2024.00033
Hu, Z., & Wu, Z. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-9440-9_31
Sun, Q., Liu, D., Dong, H., & Li, S. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5021843
DLUE: Benchmarking Document Language Understanding
Xu, R. F., Lin, H., Guan, X., Sun, Y., & Sun, L. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8367-0_23
Modeling polarization in public opinion through LLM-synthesized arguments and stance trees
Diaz, G. A., Chesñevar, C. I., Patel, R., & Maguitman, A. G. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3906
Does Discussion Matter? Interaction Dynamics in Open Peer Review
Wei, S., & Bi, R. (2026) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/3785651.3831562
- Authors
- Cheng, L., Bing, L., Yu, Q., Lu, W., & Si, L.
- Affiliations
- Singapore University of Technology and Design (SG)
- Alibaba Group (Cayman Islands) (KY)
- Chinese University of Hong Kong (HK)
- Venue
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Published
- 2020
- Pages
- 7000-7011
- OpenAlex
- W3103553006
- Cited by
- 58
- References
- 38
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 10 · 2023: 16 · 2022: 13 · 2021: 11 · 2020: 2
- FWCI
- 4.01
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Keywords
- peer review, rebuttal, argument mining, multi-task learning, hierarchical LSTM, sequence labeling
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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