Graph Attention and Interaction Network With Multi-Task Learning for Fact Verification

Abstract

Fact verification is a challenging task which requires to retrieve relevant sentences from plain texts and then take these sentences as evidences to verify given claims. Conventional methods treat sentence selection and claim verification as separate subtasks in a pipeline. Claim verification models usually analyzes the inference relationship between each retrieved sentence and the claim, and then aggregates the claim-sentence representations by graph-based reasoning methods, such as graph attention networks (GAT). In this paper, we propose a graph attention and interaction network (GAIN) for claim verification. In addition to GAT, this model includes a graph interaction network (GIN), which considers the comparative relationships among all claim-sentence representations. More importantly, a multi-task learning strategy, which combines the objectives of both sentence selection and claim verification, is designed to train the GAIN model in order to utilize the supervision information of both subtasks. Experimental results on the FEVER dataset show that the GAIN model with multi-task learning achieves a FEVER score of 73.04%, which outperforms other published models.