Incorporating Topic Aspects for Online Comment Convincingness Evaluation

Yunfan Gu, Zhongyu Wei, Maoran Xu, Hao Fu, Yang Liu, Xuanjing Huang


Abstract
In this paper, we propose to incorporate topic aspects information for online comments convincingness evaluation. Our model makes use of graph convolutional network to utilize implicit topic information within a discussion thread to assist the evaluation of convincingness of each single comment. In order to test the effectiveness of our proposed model, we annotate topic information on top of a public dataset for argument convincingness evaluation. Experimental results show that topic information is able to improve the performance for convincingness evaluation. We also make a move to detect topic aspects automatically.
Anthology ID:
W18-5212
Volume:
Proceedings of the 5th Workshop on Argument Mining
Month:
November
Year:
2018
Address:
Brussels, Belgium
Editors:
Noam Slonim, Ranit Aharonov
Venue:
ArgMining
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
97–104
Language:
URL:
https://aclanthology.org/W18-5212
DOI:
10.18653/v1/W18-5212
Bibkey:
Cite (ACL):
Yunfan Gu, Zhongyu Wei, Maoran Xu, Hao Fu, Yang Liu, and Xuanjing Huang. 2018. Incorporating Topic Aspects for Online Comment Convincingness Evaluation. In Proceedings of the 5th Workshop on Argument Mining, pages 97–104, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Incorporating Topic Aspects for Online Comment Convincingness Evaluation (Gu et al., ArgMining 2018)
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PDF:
https://preview.aclanthology.org/ingest-bitext-workshop/W18-5212.pdf