APE: Argument Pair Extraction from Peer Review and Rebuttal via Multi-task Learning

Liying Cheng, Lidong Bing, Qian Yu, Wei Lu, Luo Si

Abstract
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.
Anthology ID:
2020.emnlp-main.569
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Editors:
Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7000–7011
Language:
URL:
https://aclanthology.org/2020.emnlp-main.569
DOI:
10.18653/v1/2020.emnlp-main.569
Bibkey:
Cite (ACL):
Liying Cheng, Lidong Bing, Qian Yu, Wei Lu, and Luo Si. 2020. APE: Argument Pair Extraction from Peer Review and Rebuttal via Multi-task Learning. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7000–7011, Online. Association for Computational Linguistics.
Cite (Informal):
APE: Argument Pair Extraction from Peer Review and Rebuttal via Multi-task Learning (Cheng et al., EMNLP 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-3/2020.emnlp-main.569.pdf
Video:
 https://slideslive.com/38939058
Code
 LiyingCheng95/ArgumentPairExtraction
Data
RR
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