IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks

Liying Cheng, Lidong Bing, Ruidan He, Qian Yu, Yan Zhang, Luo Si


Abstract
Traditionally, a debate usually requires a manual preparation process, including reading plenty of articles, selecting the claims, identifying the stances of the claims, seeking the evidence for the claims, etc. As the AI debate attracts more attention these years, it is worth exploring the methods to automate the tedious process involved in the debating system. In this work, we introduce a comprehensive and large dataset named IAM, which can be applied to a series of argument mining tasks, including claim extraction, stance classification, evidence extraction, etc. Our dataset is collected from over 1k articles related to 123 topics. Near 70k sentences in the dataset are fully annotated based on their argument properties (e.g., claims, stances, evidence, etc.). We further propose two new integrated argument mining tasks associated with the debate preparation process: (1) claim extraction with stance classification (CESC) and (2) claim-evidence pair extraction (CEPE). We adopt a pipeline approach and an end-to-end method for each integrated task separately. Promising experimental results are reported to show the values and challenges of our proposed tasks, and motivate future research on argument mining.
Anthology ID:
2022.acl-long.162
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2277–2287
Language:
URL:
https://aclanthology.org/2022.acl-long.162
DOI:
10.18653/v1/2022.acl-long.162
Bibkey:
Cite (ACL):
Liying Cheng, Lidong Bing, Ruidan He, Qian Yu, Yan Zhang, and Luo Si. 2022. IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2277–2287, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks (Cheng et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.162.pdf
Software:
 2022.acl-long.162.software.zip
Video:
 https://aclanthology.org/2022.acl-long.162.mp4
Code
 liyingcheng95/iam
Data
IAM Dataset