Graph Reasoning with Context-Aware Linearization for Interpretable Fact Extraction and Verification

TL;DR

This paper introduces a graph reasoning system that uses context-aware linearization of tabular data for interpretable fact extraction and verification, demonstrating competitive performance on the FEVEROUS dataset.

Contribution

It proposes a novel framework combining graph attention networks with context-aware linearization of tables for improved fact verification interpretability.

Findings

Achieved a FEVEROUS score of 0.23

Reached 53% label accuracy on test data

Demonstrated interpretability through case studies

Abstract

This paper presents an end-to-end system for fact extraction and verification using textual and tabular evidence, the performance of which we demonstrate on the FEVEROUS dataset. We experiment with both a multi-task learning paradigm to jointly train a graph attention network for both the task of evidence extraction and veracity prediction, as well as a single objective graph model for solely learning veracity prediction and separate evidence extraction. In both instances, we employ a framework for per-cell linearization of tabular evidence, thus allowing us to treat evidence from tables as sequences. The templates we employ for linearizing tables capture the context as well as the content of table data. We furthermore provide a case study to show the interpretability our approach. Our best performing system achieves a FEVEROUS score of 0.23 and 53% label accuracy on the blind test data.