@inproceedings{nogueira-cho-2017-task,
title = "Task-Oriented Query Reformulation with Reinforcement Learning",
author = "Nogueira, Rodrigo and
Cho, Kyunghyun",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1061",
doi = "10.18653/v1/D17-1061",
pages = "574--583",
abstract = "Search engines play an important role in our everyday lives by assisting us in finding the information we need. When we input a complex query, however, results are often far from satisfactory. In this work, we introduce a query reformulation system based on a neural network that rewrites a query to maximize the number of relevant documents returned. We train this neural network with reinforcement learning. The actions correspond to selecting terms to build a reformulated query, and the reward is the document recall. We evaluate our approach on three datasets against strong baselines and show a relative improvement of 5-20{\%} in terms of recall. Furthermore, we present a simple method to estimate a conservative upper-bound performance of a model in a particular environment and verify that there is still large room for improvements.",
}
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%0 Conference Proceedings
%T Task-Oriented Query Reformulation with Reinforcement Learning
%A Nogueira, Rodrigo
%A Cho, Kyunghyun
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F nogueira-cho-2017-task
%X Search engines play an important role in our everyday lives by assisting us in finding the information we need. When we input a complex query, however, results are often far from satisfactory. In this work, we introduce a query reformulation system based on a neural network that rewrites a query to maximize the number of relevant documents returned. We train this neural network with reinforcement learning. The actions correspond to selecting terms to build a reformulated query, and the reward is the document recall. We evaluate our approach on three datasets against strong baselines and show a relative improvement of 5-20% in terms of recall. Furthermore, we present a simple method to estimate a conservative upper-bound performance of a model in a particular environment and verify that there is still large room for improvements.
%R 10.18653/v1/D17-1061
%U https://aclanthology.org/D17-1061
%U https://doi.org/10.18653/v1/D17-1061
%P 574-583
Markdown (Informal)
[Task-Oriented Query Reformulation with Reinforcement Learning](https://aclanthology.org/D17-1061) (Nogueira & Cho, EMNLP 2017)
ACL