@inproceedings{aissa-etal-2018-reinforcement,
title = "A Reinforcement Learning-driven Translation Model for Search-Oriented Conversational Systems",
author = "Aissa, Wafa and
Soulier, Laure and
Denoyer, Ludovic",
editor = "Chuklin, Aleksandr and
Dalton, Jeff and
Kiseleva, Julia and
Borisov, Alexey and
Burtsev, Mikhail",
booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SCAI}: The 2nd International Workshop on Search-Oriented Conversational {AI}",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-5705",
doi = "10.18653/v1/W18-5705",
pages = "33--39",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T A Reinforcement Learning-driven Translation Model for Search-Oriented Conversational Systems
%A Aissa, Wafa
%A Soulier, Laure
%A Denoyer, Ludovic
%Y Chuklin, Aleksandr
%Y Dalton, Jeff
%Y Kiseleva, Julia
%Y Borisov, Alexey
%Y Burtsev, Mikhail
%S Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F aissa-etal-2018-reinforcement
%X 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.
%R 10.18653/v1/W18-5705
%U https://aclanthology.org/W18-5705
%U https://doi.org/10.18653/v1/W18-5705
%P 33-39
Markdown (Informal)
[A Reinforcement Learning-driven Translation Model for Search-Oriented Conversational Systems](https://aclanthology.org/W18-5705) (Aissa et al., EMNLP 2018)
ACL