A Reinforcement Learning-driven Translation Model for Search-Oriented Conversational Systems

Wafa Aissa 1 Laure Soulier 1 Ludovic Denoyer 1
1 MLIA - Machine Learning and Information Access
LIP6
Abstract : Search-oriented conversational systems rely on information needs expressed in natural language (NL). We focus here on the understanding of NL expressions for building keyword-based queries. We propose a reinforcement-learning-driven translation model framework able to 1) learn the translation from NL expressions to queries in a supervised way, and, 2) to overcome the lack of large-scale dataset by framing the translation model as a word selection approach and injecting relevance feedback as a reward in the learning process. Experiments are carried out on two TREC datasets. We outline the effectiveness of our approach.
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Wafa Aissa, Laure Soulier, Ludovic Denoyer. A Reinforcement Learning-driven Translation Model for Search-Oriented Conversational Systems. SCAI 2018 : 2nd edition of the workshop on Search-Oriented Conversational AI, Oct 2018, Bruxelles, Belgium. pp.33 - 39. ⟨hal-02002991⟩

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