Flexibly Focusing on Supporting Facts, Using Bridge Links, and Jointly Training Specialized Modules for Multi-Hop Question Answering

Abstract

With the help of the detailed annotated question answering dataset HotpotQA, recent question answering models are trained to justify their predicted answers with supporting facts from context documents. Some related works train the same model to find supporting facts and answers jointly without having specialized models for each task. The others train separate models for each task, but do not use supporting facts effectively to find the answer; they either use only the predicted sentences and ignore the remaining context, or do not use them at all. Furthermore, while complex graph-based models consider the bridge/connection between documents in the multi-hop setting, simple BERT-based models usually drop it. We propose FlexibleFocusedReader (FFReader), a model that 1) Flexibly focuses on predicted supporting facts (SFs) without ignoring the important remaining context, 2) Focuses on the bridge between documents, despite not using graph architectures, and 3) Jointly learns predicting SFs and answering with two specialized models. Our model achieves consistent improvement over the baseline. In particular, we find that flexibly focusing on SFs is important, rather than ignoring remaining context or not using SFs at all for finding the answer. We also find that tagging the entity that links the documents at hand is very beneficial. Finally, we show that joint training is crucial for FFReader.