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

Wafa Aissa, Laure Soulier, Ludovic Denoyer


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.
Anthology ID:
W18-5705
Volume:
Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI
Month:
October
Year:
2018
Address:
Brussels, Belgium
Venues:
EMNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
33–39
Language:
URL:
https://www.aclweb.org/anthology/W18-5705
DOI:
10.18653/v1/W18-5705
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-5705.pdf