@InProceedings{wang-EtAl:2017:SIGHAN-9,
  author    = {Wang, Jianan  and  Wang, Xin  and  Li, Fang  and  Xu, Zhen  and  Wang, Zhuoran  and  Wang, Baoxun},
  title     = {Group Linguistic Bias Aware Neural Response Generation},
  booktitle = {Proceedings of the 9th SIGHAN Workshop on Chinese Language Processing},
  month     = {December},
  year      = {2017},
  address   = {Taiwan},
  publisher = {Association for Computational Linguistics},
  pages     = {1--10},
  abstract  = {For practical chatbots, one of the essential factor for improving user
	experience is the capability of customizing the talking style of the agents,
	that is, to make chatbots provide responses meeting users' preference on
	language styles, topics, etc. To address this issue, this paper proposes to
	incorporate linguistic biases, which implicitly involved in the conversation
	corpora generated by human groups in the Social Network Services (SNS), into
	the encoder-decoder based response generator. By attaching a specially designed
	neural component to dynamically control the impact of linguistic biases in
	response generation, a Group Linguistic Bias Aware Neural Response Generation
	(GLBA-NRG) model is eventually presented. The experimental results on the
	dataset from the Chinese SNS show that the proposed architecture outperforms
	the current response generating models by producing both meaningful and vivid
	responses with customized styles.},
  url       = {http://www.aclweb.org/anthology/W17-6001}
}