@InProceedings{lan-EtAl:2017:EMNLP20172,
  author    = {Lan, Man  and  Wang, Jianxiang  and  Wu, Yuanbin  and  Niu, Zheng-Yu  and  Wang, Haifeng},
  title     = {Multi-task Attention-based Neural Networks for Implicit Discourse Relationship Representation and Identification},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {1299--1308},
  abstract  = {We present a novel multi-task attention based neural network model to address
	implicit discourse relationship representation and identification through two
	types of representation learning, an attention based neural network for
	learning discourse relationship representation with two arguments and a
	multi-task framework for learning knowledge from annotated and unannotated
	corpora. The extensive experiments have been performed on two benchmark corpora
	(i.e., PDTB and CoNLL-2016 datasets). Experimental results show that our
	proposed model outperforms the state-of-the-art systems on benchmark corpora.},
  url       = {https://www.aclweb.org/anthology/D17-1134}
}