@inproceedings{wang-etal-2017-group,
    title = "Group Linguistic Bias Aware Neural Response Generation",
    author = "Wang, Jianan  and
      Wang, Xin  and
      Li, Fang  and
      Xu, Zhen  and
      Wang, Zhuoran  and
      Wang, Baoxun",
    editor = "Zhang, Yue  and
      Sui, Zhifang",
    booktitle = "Proceedings of the 9th {SIGHAN} Workshop on {C}hinese Language Processing",
    month = dec,
    year = "2017",
    address = "Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W17-6001",
    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.",
}