A Review on Fact Extraction and Verification

TL;DR

This paper reviews the fact extraction and verification task, analyzing various methods, discussing challenges, and presenting experimental results on the FEVER dataset to advance understanding and future research directions.

Contribution

It provides a comprehensive literature review, analyzes technical approaches, and presents the largest experimental study on loss functions for sentence retrieval in FEVER.

Findings

Sampling negative sentences improves retrieval performance.

Negative sampling reduces computational complexity.

Analysis highlights key challenges and future research directions.

Abstract

We study the fact checking problem, which aims to identify the veracity of a given claim. Specifically, we focus on the task of Fact Extraction and VERification (FEVER) and its accompanied dataset. The task consists of the subtasks of retrieving the relevant documents (and sentences) from Wikipedia and validating whether the information in the documents supports or refutes a given claim. This task is essential and can be the building block of applications such as fake news detection and medical claim verification. In this paper, we aim at a better understanding of the challenges of the task by presenting the literature in a structured and comprehensive way. We describe the proposed methods by analyzing the technical perspectives of the different approaches and discussing the performance results on the FEVER dataset, which is the most well-studied and formally structured dataset on the fact extraction and verification task. We also conduct the largest experimental study to date on identifying beneficial loss functions for the sentence retrieval component. Our analysis indicates that sampling negative sentences is important for improving the performance and decreasing the computational complexity. Finally, we describe open issues and future challenges, and we motivate future research in the task.