@inproceedings{sun-etal-2024-discourse,
title = "Discourse Structure-Aware Prefix for Generation-Based End-to-End Argumentation Mining",
author = "Sun, Yang and
Chen, Guanrong and
Yang, Caihua and
Bao, Jianzhu and
Liang, Bin and
Zeng, Xi and
Yang, Min and
Xu, Ruifeng",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.689",
doi = "10.18653/v1/2024.findings-acl.689",
pages = "11597--11613",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Discourse Structure-Aware Prefix for Generation-Based End-to-End Argumentation Mining
%A Sun, Yang
%A Chen, Guanrong
%A Yang, Caihua
%A Bao, Jianzhu
%A Liang, Bin
%A Zeng, Xi
%A Yang, Min
%A Xu, Ruifeng
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics: ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F sun-etal-2024-discourse
%X 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.
%R 10.18653/v1/2024.findings-acl.689
%U https://aclanthology.org/2024.findings-acl.689
%U https://doi.org/10.18653/v1/2024.findings-acl.689
%P 11597-11613
Markdown (Informal)
[Discourse Structure-Aware Prefix for Generation-Based End-to-End Argumentation Mining](https://aclanthology.org/2024.findings-acl.689) (Sun et al., Findings 2024)
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. Association for Computational Linguistics.