@InProceedings{gu-EtAl:2018:W18-52,
  author    = {Gu, Yunfan  and  Wei, Zhongyu  and  Xu, Maoran  and  Fu, Hao  and  Liu, Yang  and  Huang, Xuanjing},
  title     = {Incorporating Topic Aspects for Online Comment Convincingness Evaluation},
  booktitle = {Proceedings of the 5th Workshop on Argument Mining},
  month     = {November},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics},
  pages     = {97--104},
  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.},
  url       = {http://www.aclweb.org/anthology/W18-5212}
}