@InProceedings{kurita-kawahara-kurohashi:2017:Long,
  author    = {Kurita, Shuhei  and  Kawahara, Daisuke  and  Kurohashi, Sadao},
  title     = {Neural Joint Model for Transition-based Chinese Syntactic Analysis},
  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     = {1204--1214},
  abstract  = {We present neural network-based joint models for Chinese word segmentation, POS
	tagging and dependency parsing. Our models are the first neural approaches for
	fully joint Chinese analysis that is known to prevent the error propagation
	problem of pipeline models. Although word embeddings play a key role in
	dependency parsing, they cannot be applied directly to the joint task in the
	previous work. To address this problem, we propose embeddings of character
	strings, in addition to words. Experiments show that our models outperform
	existing systems in Chinese word segmentation and POS tagging, and perform
	preferable accuracies in dependency parsing. We also explore bi-LSTM models
	with fewer features.},
  url       = {http://aclweb.org/anthology/P17-1111}
}