Task-Oriented Query Reformulation with Reinforcement Learning

Rodrigo Nogueira, Kyunghyun Cho


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.
Anthology ID:
D17-1061
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
574–583
Language:
URL:
https://www.aclweb.org/anthology/D17-1061
DOI:
10.18653/v1/D17-1061
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1061.pdf
Video:
 https://vimeo.com/238236064