@inproceedings{chi-etal-2017-speaker,
    title = "Speaker Role Contextual Modeling for Language Understanding and Dialogue Policy Learning",
    author = "Chi, Ta-Chung  and
      Chen, Po-Chun  and
      Su, Shang-Yu  and
      Chen, Yun-Nung",
    editor = "Kondrak, Greg  and
      Watanabe, Taro",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/I17-2028/",
    pages = "163--168",
    abstract = "Language understanding (LU) and dialogue policy learning are two essential components in conversational systems. Human-human dialogues are not well-controlled and often random and unpredictable due to their own goals and speaking habits. This paper proposes a role-based contextual model to consider different speaker roles independently based on the various speaking patterns in the multi-turn dialogues. The experiments on the benchmark dataset show that the proposed role-based model successfully learns role-specific behavioral patterns for contextual encoding and then significantly improves language understanding and dialogue policy learning tasks."
}