@inproceedings{chikai-etal-2019-responsive,
    title = "Responsive and Self-Expressive Dialogue Generation",
    author = "Chikai, Kozo  and
      Takayama, Junya  and
      Arase, Yuki",
    editor = "Chen, Yun-Nung  and
      Bedrax-Weiss, Tania  and
      Hakkani-Tur, Dilek  and
      Kumar, Anuj  and
      Lewis, Mike  and
      Luong, Thang-Minh  and
      Su, Pei-Hao  and
      Wen, Tsung-Hsien",
    booktitle = "Proceedings of the First Workshop on NLP for Conversational AI",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/W19-4116/",
    doi = "10.18653/v1/W19-4116",
    pages = "139--149",
    abstract = "A neural conversation model is a promising approach to develop dialogue systems with the ability of chit-chat. It allows training a model in an end-to-end manner without complex rule design nor feature engineering. However, as a side effect, the neural model tends to generate safe but uninformative and insensitive responses like {\textquotedblleft}OK{\textquotedblright} and {\textquotedblleft}I don`t know.{\textquotedblright} Such replies are called generic responses and regarded as a critical problem for user-engagement of dialogue systems. For a more engaging chit-chat experience, we propose a neural conversation model that generates responsive and self-expressive replies. Specifically, our model generates domain-aware and sentiment-rich responses. Experiments empirically confirmed that our model outperformed the sequence-to-sequence model; 68.1{\%} of our responses were domain-aware with sentiment polarities, which was only 2.7{\%} for responses generated by the sequence-to-sequence model."
}