AQE: Argument Quadruplet Extraction via a Quad-Tagging Augmented Generative Approach

Jia Guo, Liying Cheng, Wenxuan Zhang, Stanley Kok, Xin Li, Lidong Bing

Abstract
Argument mining involves multiple sub-tasks that automatically identify argumentative elements, such as claim detection, evidence extraction, stance classification, etc. However, each subtask alone is insufficient for a thorough understanding of the argumentative structure and reasoning process. To learn a complete view of an argument essay and capture the interdependence among argumentative components, we need to know what opinions people hold (i.e., claims), why those opinions are valid (i.e., supporting evidence), which source the evidence comes from (i.e., evidence type), and how those claims react to the debating topic (i.e., stance). In this work, we for the first time propose a challenging argument quadruplet extraction task (AQE), which can provide an all-in-one extraction of four argumentative components, i.e., claims, evidence, evidence types, and stances. To support this task, we construct a large-scale and challenging dataset. However, there is no existing method that can solve the argument quadruplet extraction. To fill this gap, we propose a novel quad-tagging augmented generative approach, which leverages a quadruplet tagging module to augment the training of the generative framework. The experimental results on our dataset demonstrate the empirical superiority of our proposed approach over several strong baselines.
Anthology ID:
2023.findings-acl.59
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
932–946
Language:
URL:
https://aclanthology.org/2023.findings-acl.59
DOI:
10.18653/v1/2023.findings-acl.59
Bibkey:
Cite (ACL):
Jia Guo, Liying Cheng, Wenxuan Zhang, Stanley Kok, Xin Li, and Lidong Bing. 2023. AQE: Argument Quadruplet Extraction via a Quad-Tagging Augmented Generative Approach. In Findings of the Association for Computational Linguistics: ACL 2023, pages 932–946, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
AQE: Argument Quadruplet Extraction via a Quad-Tagging Augmented Generative Approach (Guo et al., Findings 2023)
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https://preview.aclanthology.org/nschneid-patch-5/2023.findings-acl.59.pdf
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