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
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 574–583
- Language:
- URL:
- https://aclanthology.org/D17-1061
- DOI:
- 10.18653/v1/D17-1061
- Cite (ACL):
- Rodrigo Nogueira and Kyunghyun Cho. 2017. Task-Oriented Query Reformulation with Reinforcement Learning. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 574–583, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Task-Oriented Query Reformulation with Reinforcement Learning (Nogueira & Cho, EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/D17-1061.pdf
- Code
- nyu-dl/QueryReformulator + additional community code