End-to-End Reinforcement Learning of Dialogue Agents for Information Access

@inproceedings{Dhingra2017EndtoEndRL,
  title={End-to-End Reinforcement Learning of Dialogue Agents for Information Access},
  author={Bhuwan Dhingra and Lihong Li and Xiujun Li and Jianfeng Gao and Yun-Nung Chen and Faisal Ahmed and Li Deng},
  booktitle={ACL},
  year={2017}
}
This paper proposes KB-InfoBot1 — a multi-turn dialogue agent which helps users search Knowledge Bases (KBs) without composing complicated queries. Such goal-oriented dialogue agents typically need to interact with an external database to access real-world knowledge. Previous systems achieved this by issuing a symbolic query to the KB to retrieve entries based on their attributes. However, such symbolic operations break the differentiability of the system and prevent endto-end training of… CONTINUE READING
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