Autonomous Sub-domain Modeling for Dialogue Policy with Hierarchical Deep Reinforcement Learning
文献类型：会议论文
| 作者 | Kristianto, Giovanni Yoko2; Zhang HW(张会文)1,3 ; Tong, Bin2; Iwayama, Makoto2; Kobayashi, Yoshiyuki2
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| 出版日期 | 2018 |
| 会议日期 | October 31, 2018 |
| 会议地点 | Brussels, Belgium |
| 页码 | 9-16 |
| 英文摘要 | 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. |
| 产权排序 | 2 |
| 会议录 | 2nd International Workshop on Search-Oriented Conversational AI, SCAI 2018
 |
| 会议录出版者 | Association for Computational Linguistics |
| 会议录出版地 | Stroudsburg PA, USA |
| 语种 | 英语 |
| ISBN号 | 978-1-948087-75-9 |
| 源URL | [http://ir.sia.cn/handle/173321/30279]  |
| 专题 | 沈阳自动化研究所_空间自动化技术研究室 |
| 通讯作者 | Kristianto, Giovanni Yoko |
| 作者单位 | 1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China 2.Hitachi Central Research Laboratory, Tokyo, Japan 3.University of Chinese Academy of Sciences, Beijing, China |
| 推荐引用方式 GB/T 7714 | Kristianto, Giovanni Yoko,Zhang HW,Tong, Bin,et al. Autonomous Sub-domain Modeling for Dialogue Policy with Hierarchical Deep Reinforcement Learning[C]. 见:. Brussels, Belgium. October 31, 2018. |
入库方式： OAI收割
来源：沈阳自动化研究所
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