@InProceedings{kristianto-EtAl:2018:SCAI,
  author    = {Kristianto, Giovanni Yoko  and  Zhang, Huiwen  and  Tong, Bin  and  Iwayama, Makoto  and  Kobayashi, Yoshiyuki},
  title     = {Autonomous Sub-domain Modeling for Dialogue Policy with Hierarchical Deep Reinforcement Learning},
  booktitle = {Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI},
  month     = {October},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics},
  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 sub-domains by 11% in terms of success rate, and is competitive with that with manually defined sub-domains.},
  url       = {http://www.aclweb.org/anthology/W18-5702}
}