@inproceedings{kristianto-etal-2018-autonomous,
    title = "Autonomous Sub-domain Modeling for Dialogue Policy with Hierarchical Deep Reinforcement Learning",
    author = "Kristianto, Giovanni Yoko  and
      Zhang, Huiwen  and
      Tong, Bin  and
      Iwayama, Makoto  and
      Kobayashi, Yoshiyuki",
    editor = "Chuklin, Aleksandr  and
      Dalton, Jeff  and
      Kiseleva, Julia  and
      Borisov, Alexey  and
      Burtsev, Mikhail",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SCAI}: The 2nd International Workshop on Search-Oriented Conversational {AI}",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W18-5702",
    doi = "10.18653/v1/W18-5702",
    pages = "9--16",
    abstract = "Solving composites tasks, which consist of several inherent sub-tasks, remains a challenge in the research area of dialogue. Current studies have tackled this issue by manually decomposing the composite tasks into several sub-domains. However, much human effort is inevitable. This paper proposes a dialogue framework that autonomously models meaningful sub-domains and learns the policy over them. Our experiments show that our framework outperforms the baseline without subdomains by 11{\%} in terms of success rate, and is competitive with that with manually defined sub-domains.",
}