We propose a novel, path-based reasoning approach for the multi-hop reading
comprehension task where a system needs to combine facts from multiple passages
to answer a question. Although inspired by multi-hop reasoning over knowledge
graphs, our proposed approach operates directly over unstructured text. It
generates potential paths through passages and scores them without any direct
path supervision. The proposed model, named PathNet, attempts to extract
implicit relations from text through entity pair representations, and compose
them to encode each path. To capture additional context, PathNet also composes
the passage representations along each path to compute a passage-based
representation. Unlike previous approaches, our model is then able to explain
its reasoning via these explicit paths through the passages. We show that our
approach outperforms prior models on the multi-hop Wikihop dataset, and also
can be generalized to apply to the OpenBookQA dataset, matching
state-of-the-art performance.

本研究提出了一种基于路径的推理方法，用于多跳阅读理解任务，该方法结合了多个段落的事实来回答问题，并以 PathNet 模型为例，使用实体对表示方法从文本中提取隐含关系并编码路径。我们展示了该方法在多跳 Wikihop 数据集上的优异表现，并且可以推广到 OpenBookQA 数据集，达到最先进的性能水平。