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- W2890208654 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 in the learning process. Experiments are carried out on two TREC datasets and outline the effectiveness of our approach." @default.
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- W2890208654 date "2018-08-29" @default.
- W2890208654 modified "2023-09-23" @default.
- W2890208654 title "A Reinforcement Learning-driven Translation Model for Search-Oriented Conversational Systems." @default.
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