Abstract
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 stateof-the-art performance.