Dual-Channel Reasoning Model for Complex Question Answering

Abstract

Multihop question answering has attracted extensive studies in recent years because of the emergence of human annotated datasets and associated leaderboards. Recent studies have revealed that question answering systems learn to exploit annotation artifacts and other biases in current datasets. Therefore, a model with strong interpretability should not only predict the final answer, but more importantly find the supporting facts' sentences necessary to answer complex questions, also known as evidence sentences. Most existing methods predict the final answer and evidence sentences in sequence or simultaneously, which inhibits the ability of models to predict the path of reasoning. In this paper, we propose a dual-channel reasoning architecture, where two reasoning channels predict the final answer and supporting facts' sentences, respectively, while sharing the contextual embedding layer. The two reasoning channels can simply use the same reasoning structure without additional network designs. Through experimental analysis based on public question answering datasets, we demonstrate the effectiveness of our proposed method