Exploiting Explicit Paths for Multi-hop Reading Comprehension

Understand

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.