@InProceedings{ji-EtAl:2018:C18-1,
  author    = {Ji, Lu  and  Wei, Zhongyu  and  Hu, Xiangkun  and  Liu, Yang  and  Zhang, Qi  and  Huang, Xuanjing},
  title     = {Incorporating Argument-Level Interactions for Persuasion Comments Evaluation using Co-attention Model},
  booktitle = {Proceedings of the 27th International Conference on Computational Linguistics},
  month     = {August},
  year      = {2018},
  address   = {Santa Fe, New Mexico, USA},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {http://www.aclweb.org/anthology/C18-1314}
}