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.

本文研究事实检查问题，特别是 Fact Extraction and VERification (FEVER) 任务及其相关数据集。通过分析不同方法的技术视角和在 FEVER 数据集上的性能结果，我们描述了所提出的方法，重点讨论了句子检索部分的有益损失函数的识别，最终描述了未来研究的开放问题和挑战。

事实提取与验证综述

A Review on Fact Extraction and Verification

Recent work has suggested that language models (LMs) store both common-sense
and factual knowledge learned from pre-training data. In this paper, we
leverage this implicit knowledge to create an effective end-to-end fact checker
using a solely a language model, without any external knowledge or explicit
retrieval components. While previous work on extracting knowledge from LMs have
focused on the task of open-domain question answering, to the best of our
knowledge, this is the first work to examine the use of language models as fact
checkers. In a closed-book setting, we show that our zero-shot LM approach
outperforms a random baseline on the standard FEVER task, and that our
fine-tuned LM compares favorably with standard baselines. Though we do not
ultimately outperform methods which use explicit knowledge bases, we believe
our exploration shows that this method is viable and has much room for
exploration.

本文探索了利用语言模型自带的知识来创建基于 LM 的事实检查器的方法，并在闭卷条件下展示了我们的零 - shot LM 方法在标准 FEVER 任务上优于随机基准线，而我们的微调 LM 与标准基准线相比表现出色。虽然我们最终没有超越使用显式知识库的方法，但我们相信我们的探索显示出这种方法是可行的，并且有很大的发展空间。