  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.
