@InProceedings{wang-EtAl:2017:I17-12,
  author    = {Wang, Yizhong  and  Li, Sujian  and  Yang, Jingfeng  and  Sun, Xu  and  Wang, Houfeng},
  title     = {Tag-Enhanced Tree-Structured Neural Networks for Implicit Discourse Relation Classification},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {496--505},
  abstract  = {Identifying implicit discourse relations between text spans is a challenging
	task because it requires understanding the meaning of the text. To tackle this
	task, recent studies have tried several deep learning methods but few of them
	exploited the syntactic information. In this work, we explore the idea of
	incorporating syntactic parse tree into neural networks. Specifically, we
	employ the Tree-LSTM model and Tree-GRU model, which is based on the tree
	structure, to encode the arguments in a relation. And we further leverage the
	constituent tags to control the semantic composition process in these
	tree-structured neural networks. Experimental results show that our method
	achieves state-of-the-art performance on PDTB corpus.},
  url       = {http://www.aclweb.org/anthology/I17-1050}
}