@inproceedings{takayama-arase-2019-relevant,
    title = "Relevant and Informative Response Generation using Pointwise Mutual Information",
    author = "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/iwcs-25-ingestion/W19-4115/",
    doi = "10.18653/v1/W19-4115",
    pages = "133--138",
    abstract = "A sequence-to-sequence model tends to generate generic responses with little information for input utterances. To solve this problem, we propose a neural model that generates relevant and informative responses. Our model has simple architecture to enable easy application to existing neural dialogue models. Specifically, using positive pointwise mutual information, it first identifies keywords that frequently co-occur in responses given an utterance. Then, the model encourages the decoder to use the keywords for response generation. Experiment results demonstrate that our model successfully diversifies responses relative to previous models."
}