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

Abstract

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. We also empirically show its effectiveness and scalability on CommonsenseQA and OpenbookQA datasets, and interpret its behaviors with case studies.

Accepted to EMNLP 2020. Project page: https://github.com/INK-USC/MHGRN

Cited by in corpus (2)

• Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering
• A Universal Model for Cross Modality Mapping by Relational Reasoning