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

中文翻译：

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复杂问答的双通道推理模型

近年来，由于人工注释数据集和相关排行榜的出现，多跳问答吸引了广泛的研究。最近的研究表明，问答系统学会了利用当前数据集中的注释工件和其他偏差。因此，具有强可解释性的模型不仅应该预测最终答案，更重要的是找到回答复杂问题所需的支持事实的句子，也称为证据句子。大多数现有方法按顺序或同时预测最终答案和证据句子，这抑制了模型预测推理路径的能力。在本文中，我们提出了一种双通道推理架构，其中两个推理通道分别预测最终答案和支持事实的句子，同时共享上下文嵌入层。两个推理通道可以简单地使用相同的推理结构，无需额外的网络设计。通过基于公共问答数据集的实验分析，我们证明了我们提出的方法的有效性