@InProceedings{yin-EtAl:2017:EMNLP2017,
  author    = {Yin, Qingyu  and  Zhang, Yu  and  Zhang, Weinan  and  Liu, Ting},
  title     = {Chinese Zero Pronoun Resolution with Deep Memory Network},
  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     = {1309--1318},
  abstract  = {Existing approaches for Chinese zero pronoun resolution typically utilize only
	syntactical and lexical features while ignoring semantic information. The
	fundamental reason is that zero pronouns have no descriptive information, which
	brings difficulty in explicitly capturing their semantic similarities with
	antecedents. Meanwhile, representing zero pronouns is challenging since they
	are merely gaps that convey no actual content. In this paper, we address this
	issue by building a deep memory network that is capable of encoding zero
	pronouns into vector representations with information obtained from their
	contexts and potential antecedents. Consequently, our resolver takes advantage
	of semantic information by using these continuous distributed representations.
	Experiments on the OntoNotes 5.0 dataset show that the proposed memory network
	could substantially outperform the state-of-the-art systems in various
	experimental settings.},
  url       = {https://www.aclweb.org/anthology/D17-1135}
}