  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.
