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

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 in the learning process. Experiments are carried out on two TREC datasets and outline the effectiveness of our approach.Comment: This is the author's pre-print version of the work. It is posted here for your personal use, not for redistribution. Please cite the definitive version which will be published in Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI - ISBN: 978-1-948087-75-