A Structure-Aware Argument Encoder for Literature Discourse Analysis

Yinzi Li, Wei Chen, Zhongyu Wei, Yujun Huang, Chujun Wang, Siyuan Wang, Qi Zhang, Xuanjing Huang, Libo Wu


Abstract
Existing research for argument representation learning mainly treats tokens in the sentence equally and ignores the implied structure information of argumentative context. In this paper, we propose to separate tokens into two groups, namely framing tokens and topic ones, to capture structural information of arguments. In addition, we consider high-level structure by incorporating paragraph-level position information. A novel structure-aware argument encoder is proposed for literature discourse analysis. Experimental results on both a self-constructed corpus and a public corpus show the effectiveness of our model. Resources are available at https://github.com/lemuria-wchen/SAE.
Anthology ID:
2022.coling-1.619
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
7093–7098
Language:
URL:
https://aclanthology.org/2022.coling-1.619
DOI:
Bibkey:
Cite (ACL):
Yinzi Li, Wei Chen, Zhongyu Wei, Yujun Huang, Chujun Wang, Siyuan Wang, Qi Zhang, Xuanjing Huang, and Libo Wu. 2022. A Structure-Aware Argument Encoder for Literature Discourse Analysis. In Proceedings of the 29th International Conference on Computational Linguistics, pages 7093–7098, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
A Structure-Aware Argument Encoder for Literature Discourse Analysis (Li et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.619.pdf
Code
 lemuria-wchen/sae