@InProceedings{huang-wang:2016:NLPTEA2016,
  author    = {Huang, Shen  and  WANG, Houfeng},
  title     = {Bi-LSTM Neural Networks for Chinese Grammatical Error Diagnosis},
  booktitle = {Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {148--154},
  abstract  = {Grammatical Error Diagnosis for Chinese has always been a challenge for both
	foreign learners and NLP researchers, for the variousity of grammar and the
	flexibility of expression. In this paper, we present a model based on
	Bidirectional Long Short-Term Memory(Bi-LSTM) neural networks, which treats the
	task as a sequence labeling problem, so as to detect Chinese grammatical
	errors, to identify the error types and to locate the error positions. In the
	corpora of this year's shared task, there can be multiple errors in a single
	offset of a sentence, to address which, we simutaneously train three Bi-LSTM
	models sharing word embeddings which label Missing, Redundant and Selection
	errors respectively. We regard word ordering error as a special kind of word
	selection error which is longer during training phase, and then separate them
	by length during testing phase.
	  In NLP-TEA 3 shared task for Chinese Grammatical Error Diagnosis(CGED), Our
	system achieved relatively high F1 for all the three levels in the traditional
	Chinese track and for the detection level in the Simpified Chinese track.},
  url       = {http://aclweb.org/anthology/W16-4919}
}