  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.
