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SimpleDS: A Simple Deep Reinforcement Learning Dialogue System

  • Heriberto CuayáhuitlEmail author
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 427)

Abstract

This article presents SimpleDS, a simple and publicly available dialogue system trained with deep reinforcement learning. In contrast to previous reinforcement learning dialogue systems, this system avoids manual feature engineering by performing action selection directly from raw text of the last system and (noisy) user responses. Our initial results, in the restaurant domain, report that it is indeed possible to induce reasonable behaviours with such an approach that aims for higher levels of automation in dialogue control for intelligent interactive systems and robots.

Keywords

Dialogue systems Reinforcement learning Deep learning 

Notes

Acknowledgements

Funding from the European Research Council (ERC) project “STAC: Strategic Conversation” no. 269427 is gratefully acknowledged.

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Copyright information

© Springer Science+Business Media Singapore 2017

Authors and Affiliations

  1. 1.School of Computer ScienceUniversity of LincolnLincolnUK

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