@InProceedings{chen-EtAl:2017:Long2,
  author    = {Chen, Xinchi  and  Shi, Zhan  and  Qiu, Xipeng  and  Huang, Xuanjing},
  title     = {Adversarial Multi-Criteria Learning for Chinese Word Segmentation},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {1193--1203},
  abstract  = {Different linguistic perspectives causes many diverse segmentation criteria for
	Chinese word segmentation (CWS). Most existing methods focus on improve the
	performance for each single criterion. However, it is interesting to exploit
	these different criteria and mining their common underlying knowledge. In this
	paper, we propose adversarial multi-criteria learning for CWS by integrating
	shared knowledge from multiple heterogeneous segmentation criteria. 
	Experiments on eight corpora with heterogeneous segmentation criteria show that
	the performance of each corpus obtains a significant improvement, compared to
	single-criterion learning. Source codes of this paper are available on Github.},
  url       = {http://aclweb.org/anthology/P17-1110}
}