@inproceedings{ji-etal-2018-incorporating,
title = "Incorporating Argument-Level Interactions for Persuasion Comments Evaluation using Co-attention Model",
author = "Ji, Lu and
Wei, Zhongyu and
Hu, Xiangkun and
Liu, Yang and
Zhang, Qi and
Huang, Xuanjing",
editor = "Bender, Emily M. and
Derczynski, Leon and
Isabelle, Pierre",
booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
month = aug,
year = "2018",
address = "Santa Fe, New Mexico, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/C18-1314",
pages = "3703--3714",
abstract = "In this paper, we investigate the issue of persuasiveness evaluation for argumentative comments. Most of the existing research explores different text features of reply comments on word level and ignores interactions between participants. In general, viewpoints are usually expressed by multiple arguments and exchanged on argument level. To better model the process of dialogical argumentation, we propose a novel co-attention mechanism based neural network to capture the interactions between participants on argument level. Experimental results on a publicly available dataset show that the proposed model significantly outperforms some state-of-the-art methods for persuasiveness evaluation. Further analysis reveals that attention weights computed in our model are able to extract interactive argument pairs from the original post and the reply.",
}
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<abstract>In this paper, we investigate the issue of persuasiveness evaluation for argumentative comments. Most of the existing research explores different text features of reply comments on word level and ignores interactions between participants. In general, viewpoints are usually expressed by multiple arguments and exchanged on argument level. To better model the process of dialogical argumentation, we propose a novel co-attention mechanism based neural network to capture the interactions between participants on argument level. Experimental results on a publicly available dataset show that the proposed model significantly outperforms some state-of-the-art methods for persuasiveness evaluation. Further analysis reveals that attention weights computed in our model are able to extract interactive argument pairs from the original post and the reply.</abstract>
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%0 Conference Proceedings
%T Incorporating Argument-Level Interactions for Persuasion Comments Evaluation using Co-attention Model
%A Ji, Lu
%A Wei, Zhongyu
%A Hu, Xiangkun
%A Liu, Yang
%A Zhang, Qi
%A Huang, Xuanjing
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F ji-etal-2018-incorporating
%X In this paper, we investigate the issue of persuasiveness evaluation for argumentative comments. Most of the existing research explores different text features of reply comments on word level and ignores interactions between participants. In general, viewpoints are usually expressed by multiple arguments and exchanged on argument level. To better model the process of dialogical argumentation, we propose a novel co-attention mechanism based neural network to capture the interactions between participants on argument level. Experimental results on a publicly available dataset show that the proposed model significantly outperforms some state-of-the-art methods for persuasiveness evaluation. Further analysis reveals that attention weights computed in our model are able to extract interactive argument pairs from the original post and the reply.
%U https://aclanthology.org/C18-1314
%P 3703-3714
Markdown (Informal)
[Incorporating Argument-Level Interactions for Persuasion Comments Evaluation using Co-attention Model](https://aclanthology.org/C18-1314) (Ji et al., COLING 2018)
ACL