@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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Autonomous Sub-domain Modeling for Dialogue Policy with Hierarchical Deep Reinforcement Learning
%A Kristianto, Giovanni Yoko
%A Zhang, Huiwen
%A Tong, Bin
%A Iwayama, Makoto
%A Kobayashi, Yoshiyuki
%Y Chuklin, Aleksandr
%Y Dalton, Jeff
%Y Kiseleva, Julia
%Y Borisov, Alexey
%Y Burtsev, Mikhail
%S Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F kristianto-etal-2018-autonomous
%X 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.
%R 10.18653/v1/W18-5702
%U https://aclanthology.org/W18-5702
%U https://doi.org/10.18653/v1/W18-5702
%P 9-16
Markdown (Informal)
[Autonomous Sub-domain Modeling for Dialogue Policy with Hierarchical Deep Reinforcement Learning](https://aclanthology.org/W18-5702) (Kristianto et al., EMNLP 2018)
ACL