I Know What You Asked: Graph Path Learning using AMR for Commonsense Reasoning

TL;DR

This paper introduces a novel approach for commonsense reasoning in QA tasks using an AMR-ConceptNet pruned graph to interpret reasoning paths and improve answer prediction accuracy.

Contribution

It proposes the ACP graph that combines AMR and ConceptNet, enabling semantic interpretation and enhanced reasoning in commonsense QA.

Findings

ACP-based models outperform baselines

The ACP graph effectively interprets reasoning paths

Enhanced semantic understanding improves accuracy

Abstract

CommonsenseQA is a task in which a correct answer is predicted through commonsense reasoning with pre-defined knowledge. Most previous works have aimed to improve the performance with distributed representation without considering the process of predicting the answer from the semantic representation of the question. To shed light upon the semantic interpretation of the question, we propose an AMR-ConceptNet-Pruned (ACP) graph. The ACP graph is pruned from a full integrated graph encompassing Abstract Meaning Representation (AMR) graph generated from input questions and an external commonsense knowledge graph, ConceptNet (CN). Then the ACP graph is exploited to interpret the reasoning path as well as to predict the correct answer on the CommonsenseQA task. This paper presents the manner in which the commonsense reasoning process can be interpreted with the relations and concepts provided by the ACP graph. Moreover, ACP-based models are shown to outperform the baselines.