Knowledge-Enhanced Graph Attention Network for Fact Verification

Chonghao Chen,Jianming Zheng,Honghui Chen

doi:10.3390/math9161949

Abstract

Fact verification aims to evaluate the authenticity of a given claim based on the evidence sentences retrieved from Wikipedia articles. Existing works mainly leverage the natural language inference methods to model the semantic interaction of claim and evidence, or further employ the graph structure to capture the relation features between multiple evidences. However, previous methods have limited representation ability in encoding complicated units of claim and evidences, and thus cannot support sophisticated reasoning. In addition, a limited amount of supervisory signals lead to the graph encoder could not distinguish the distinctions of different graph structures and weaken the encoding ability. To address the above issues, we propose a Knowledge-Enhanced Graph Attention network (KEGA) for fact verification, which introduces a knowledge integration module to enhance the representation of claims and evidences by incorporating external knowledge. Moreover, KEGA leverages an auxiliary loss based on contrastive learning to fine-tune the graph attention encoder and learn the discriminative features for the evidence graph. Comprehensive experiments conducted on FEVER, a large-scale benchmark dataset for fact verification, demonstrate the superiority of our proposal in both the multi-evidences and single-evidence scenarios. In addition, our findings show that the background knowledge for words can effectively improve the model performance.

Highlights

The rapid development of social media allows more individuals to share their opinions and findings
To deal with the above issues, we propose a Knowledge-Enhanced Graph Attention network (KEGA) for fact verification, which incorporates external background knowledge for words, and introduces an auxiliary loss based on contrastive learning to help the graph encoder learn discriminative representations for evidence graphs
We propose a knowledge-enhanced graph attention network (KEGA)

Summary

Introduction

The rapid development of social media allows more individuals to share their opinions and findings. Previous works typically view all evidence sentences as an ensemble and concatenate them with the claim to obtain the overall similarity score [1,4], or compute the individual similarity for each claim–evidence pair and aggregate them as the final result [5,6] Such traditional NLI methods cannot deal with the claims that need multiple evidences to verify, since they fail to model the semantic relations of evidences. To achieve the goal of evidence reasoning, we construct the relation graph for evidences, which considers the entities as nodes and leverages their co-occurrence relation to build edges On this basis, we use the graph attention mechanism to encode the relation feature of semantic units in evidences and adopt a mixture aggregator to obtain the graph representation.

Methods

Findings

Discussion

Conclusion