Task-Oriented Query Reformulation with Reinforcement Learning
This work addresses the issue of unsatisfactory search results for users with complex queries, though it is incremental as it builds on existing methods with specific gains.
The paper tackles the problem of improving search engine results for complex queries by introducing a neural network-based query reformulation system trained with reinforcement learning, achieving a relative improvement of 5-20% in recall on three datasets.
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.