Discourse Structure-Aware Prefix for Generation-Based End-to-End Argumentation Mining

Yang Sun, Guanrong Chen, Caihua Yang, Jianzhu Bao, Bin Liang, Xi Zeng, Min Yang, Ruifeng Xu

Abstract
End-to-end argumentation mining (AM) aims to extract the argumentation structure including argumentation components and their argumentation relations from text. Recent developments in end-to-end AM models have demonstrated significant progress by redefining the AM task as a sequence generation task, exhibiting simplicity and competitive performance. Nevertheless, these models overlook the integration of supplementary discourse structure information, a crucial factor for comprehending argumentation structures, resulting in suboptimal outcomes. In this study, we propose the DENIM framework, which generates discourse structure-aware prefixes for each layer of the generation model. These prefixes imbue the generation-based AM model with discourse structures, thereby augmenting the overall generation process. Moreover, we introduce a multi-task prompt coupled with a three-step decoding strategy, aiming to optimize the efficiency and effectiveness of argumentation structure decoding. Extensive experiments and analyses on two benchmark datasets show that DENIM achieves state-of-the-art performances on two AM benchmarks.
Anthology ID:
2024.findings-acl.689
Volume:
Findings of the Association for Computational Linguistics ACL 2024
Month:
August
Year:
2024
Address:
Bangkok, Thailand and virtual meeting
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
11597–11613
Language:
URL:
https://aclanthology.org/2024.findings-acl.689
DOI:
10.18653/v1/2024.findings-acl.689
Bibkey:
Cite (ACL):
Yang Sun, Guanrong Chen, Caihua Yang, Jianzhu Bao, Bin Liang, Xi Zeng, Min Yang, and Ruifeng Xu. 2024. Discourse Structure-Aware Prefix for Generation-Based End-to-End Argumentation Mining. In Findings of the Association for Computational Linguistics ACL 2024, pages 11597–11613, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.
Cite (Informal):
Discourse Structure-Aware Prefix for Generation-Based End-to-End Argumentation Mining (Sun et al., Findings 2024)
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