Multi-hop question answering (QA) requires a model to retrieve and integrate
information from different parts of a long text to answer a question. Humans
answer this kind of complex questions via a divide-and-conquer approach. In
this paper, we investigate whether top-performing models for multi-hop
questions understand the underlying sub-questions like humans. We adopt a
neural decomposition model to generate sub-questions for a multi-hop complex
question, followed by extracting the corresponding sub-answers. We show that
multiple state-of-the-art multi-hop QA models fail to correctly answer a large
portion of sub-questions, although their corresponding multi-hop questions are
correctly answered. This indicates that these models manage to answer the
multi-hop questions using some partial clues, instead of truly understanding
the reasoning paths. We also propose a new model which significantly improves
the performance on answering the sub-questions. Our work takes a step forward
towards building a more explainable multi-hop QA system.

本文研究了多跳问题回答的模型，调研表明多个现有的多跳 QA 模型无法准确回答子问题，说明其只是通过一些简单的线索来回答问题，而非真正理解推理路径，同时，本文提出的神经解析模型能显著提高子问题的回答性能，推动了向可解释的多跳 QA 系统的建设迈进。