MSReNet: Multi-step Reformulation for Open-Domain Question Answering

Abstract

Recent works on open-domain question answering (QA) rely on retrieving related passages to answer questions. However, most of them can not escape from sub-optimal initial retrieval results because of lacking interaction with the retrieval system. This paper introduces a new framework MSReNet for open-domain question answering where the question reformulator interacts with the term-based retrieval system, which can improve retrieval precision and QA performance. Specifically, we enhance the open-domain QA model with an additional multi-step reformulator which generates a new human-readable question with the current passages and question. The interaction continues for several times before answer extraction to find the optimal retrieval results as much as possible. Experiments show MSReNet gains performance improvements on several datasets such as TriviaQA-unfiltered, Quasar-T, SearchQA, and SQuAD-open. We also find that the intermediate reformulation results provide interpretability for the reasoning process of the model.