A Reinforcement Learning-driven Translation Model for Search-Oriented Conversational Systems
Wafa Aissa, Laure Soulier, Ludovic Denoyer · 2018
Search-oriented conversational systems rely on information needs expressed in natural language (NL).We focus here on the understanding of NL expressions for building keywordbased queries.We propose a reinforcementlearning-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.