Task-Oriented Query Reformulation with Reinforcement Learning

2017-04-15 · EMNLP 2017 9 · Rodrigo Nogueira, Kyunghyun Cho

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.

📄 PDF Abstract BibTeX arXiv:1704.04572

Code (2)

nyu-dl/QueryReformulator 공식 구현

nyu-dl/dl4ir-query-reformulator

Tasks

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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