Exploring Discourse Structures for Argument Impact Classification
Xin Liu, Jiefu Ou, Yangqiu Song, Xin Jiang
Abstract
Discourse relations among arguments reveal logical structures of a debate conversation. However, no prior work has explicitly studied how the sequence of discourse relations influence a claim’s impact. This paper empirically shows that the discourse relations between two arguments along the context path are essential factors for identifying the persuasive power of an argument. We further propose DisCOC to inject and fuse the sentence-level structural discourse information with contextualized features derived from large-scale language models. Experimental results and extensive analysis show that the attention and gate mechanisms that explicitly model contexts and texts can indeed help the argument impact classification task defined by Durmus et al. (2019), and discourse structures among the context path of the claim to be classified can further boost the performance.
BibTeX
@inproceedings{liu-etal-2021-exploring-discourse,
title = "Exploring Discourse Structures for Argument Impact Classification",
author = "Liu, Xin and
Ou, Jiefu and
Song, Yangqiu and
Jiang, Xin",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "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 = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.306/",
doi = "10.18653/v1/2021.acl-long.306",
pages = "3958--3969"
}