|| VARIATIONAL DENOISING AUTOENCODERS AND LEAST-SQUARES POLICY ITERATION FOR STATISTICAL DIALOGUE MANAGERS
||Vassilios Diakoloukas, Technical University of Crete, Greece; Fotios Lygerakis, University of Texas at Arlington, United States; Michail Lagoudakis, Technical University of Crete, Greece; Margarita Kotti, Deloitte UK, United Kingdom|
|Session||HLT-3: Dialogue Systems 1: General Topics|
|Session Time:||Tuesday, 08 June, 14:00 - 14:45|
|Presentation Time:||Tuesday, 08 June, 14:00 - 14:45|
|| Human Language Technology: [HLT-DIAL] Discourse and Dialog|
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|| The use of Reinforcement Learning (RL) approaches for dialogue policy optimization has been the new trend for dialogue management systems. Several methods have been proposed, which are trained on dialogue data to provide optimal system response. However, most of these approaches exhibit performance degradation in the presence of noise, poor scalability to other domains, as well as performance instabilities. To overcome these problems, we propose a novel approach based on the incremental, sample-efficient Least-Squares Policy Iteration (LSPI) algorithm, which is trained on compact, fixed-size dialogue state encodings, obtained from deep Variational Denoising Autoencoders (VDAE). The proposed scheme exhibits stable and noise-robust performance, which significantly outperforms the current state-of-theart, even in mismatched noise environments.