Autonomous Sub-domain Modeling for Dialogue Policy with Hierarchical Deep Reinforcement Learning

Giovanni Yoko Kristianto, Huiwen Zhang, Bin Tong, Makoto Iwayama, Yoshiyuki Kobayashi


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.
Anthology ID:
W18-5702
Volume:
Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI
Month:
October
Year:
2018
Address:
Brussels, Belgium
Venues:
EMNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9–16
Language:
URL:
https://www.aclweb.org/anthology/W18-5702
DOI:
10.18653/v1/W18-5702
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-5702.pdf