Task-Oriented Query Reformulation with Reinforcement Learning
Rodrigo Nogueira, Kyunghyun Cho · 2017
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 upperbound performance of a model in a particular environment and verify that there is still large room for improvements.