AMREx: AMR for Explainable Fact Verification

TL;DR

AMREx is an explainable fact verification system that uses Abstract Meaning Representation to improve accuracy and provide interpretable justifications, aiding in reducing misinformation spread on social media.

Contribution

It introduces an AMR-based approach for fact verification that offers partially explainable justifications and enhances accuracy over existing baselines.

Findings

AMREx surpasses baseline accuracy on AVeriTeC dataset.

It provides interpretable AMR node mappings for explanations.

AMREx can guide LLMs to generate more accurate natural-language explanations.

Abstract

With the advent of social media networks and the vast amount of information circulating through them, automatic fact verification is an essential component to prevent the spread of misinformation. It is even more useful to have fact verification systems that provide explanations along with their classifications to ensure accurate predictions. To address both of these requirements, we implement AMREx, an Abstract Meaning Representation (AMR)-based veracity prediction and explanation system for fact verification using a combination of Smatch, an AMR evaluation metric to measure meaning containment and textual similarity, and demonstrate its effectiveness in producing partially explainable justifications using two community standard fact verification datasets, FEVER and AVeriTeC. AMREx surpasses the AVeriTec baseline accuracy showing the effectiveness of our approach for real-world claim verification. It follows an interpretable pipeline and returns an explainable AMR node mapping to clarify the system's veracity predictions when applicable. We further demonstrate that AMREx output can be used to prompt LLMs to generate natural-language explanations using the AMR mappings as a guide to lessen the probability of hallucinations.