@InProceedings{bao-EtAl:2017:SIGHAN-9,
  author    = {Bao, Zuyi  and  Li, Si  and  XU, Weiran  and  GAO, Sheng},
  title     = {Neural Regularized Domain Adaptation for Chinese Word Segmentation},
  booktitle = {Proceedings of the 9th SIGHAN Workshop on Chinese Language Processing},
  month     = {December},
  year      = {2017},
  address   = {Taiwan},
  publisher = {Association for Computational Linguistics},
  pages     = {11--20},
  abstract  = {For Chinese word segmentation, the large-scale annotated corpora mainly focus
	on newswire and only a handful of annotated data is available in other domains
	such as patents and literature. Considering the limited amount of annotated
	target domain data, it is a challenge for segmenters to learn domain-specific
	information while avoid getting over-fitted at the same time. In this paper, we
	propose a neural regularized domain adaptation method for Chinese word
	segmentation. The teacher networks trained in source domain are employed to
	regularize the training process of the student network by preserving the
	general knowledge. In the experiments, our neural regularized domain adaptation
	method achieves a better performance comparing to previous methods.},
  url       = {http://www.aclweb.org/anthology/W17-6002}
}