Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering

Understand

Existing work on augmenting question answering (QA) models with external knowledge (e.g., knowledge graphs) either struggle to model multi-hop relations efficiently, or lack transparency into the model's prediction rationale.

• In this paper, we propose a novel knowledge-aware approach that equips pre-trained language models (PTLMs) with a multi-hop relational reasoning module, named multi-hop graph relation network (MHGRN).
• It performs multi-hop, multi-relational reasoning over subgraphs extracted from external knowledge graphs.
• The proposed reasoning module unifies path-based reasoning methods and graph neural networks to achieve better interpretability and scalability.