A Neural Transition-based Model for Argumentation Mining

Jianzhu Bao, Chuang Fan, Jipeng Wu, Yixue Dang, Jiachen Du, Ruifeng Xu


Abstract
The goal of argumentation mining is to automatically extract argumentation structures from argumentative texts. Most existing methods determine argumentative relations by exhaustively enumerating all possible pairs of argument components, which suffer from low efficiency and class imbalance. Moreover, due to the complex nature of argumentation, there is, so far, no universal method that can address both tree and non-tree structured argumentation. Towards these issues, we propose a neural transition-based model for argumentation mining, which incrementally builds an argumentation graph by generating a sequence of actions, avoiding inefficient enumeration operations. Furthermore, our model can handle both tree and non-tree structured argumentation without introducing any structural constraints. Experimental results show that our model achieves the best performance on two public datasets of different structures.
Anthology ID:
2021.acl-long.497
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6354–6364
Language:
URL:
https://aclanthology.org/2021.acl-long.497
DOI:
10.18653/v1/2021.acl-long.497
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.497.pdf
Data
CDCP