As online false information continues to grow, automated fact-checking has
gained an increasing amount of attention in recent years. Researchers in the
field of Natural Language Processing (NLP) have contributed to the task by
building fact-checking datasets, devising automated fact-checking pipelines and
proposing NLP methods to further research in the development of different
components. This paper reviews relevant research on automated fact-checking
covering both the claim detection and claim validation components.

该篇论文回顾了涉及自动事实检查的相关研究，包括索取检测和索求验证组件，并探讨了自然语言处理，自动化事实检查的数据集以及提出的各种 NLP 方法在该领域发展方面所做的贡献。

自动事实核查：一项调查研究

Automated Fact-Checking: A Survey

A number of exciting advances have been made in automated fact-checking
thanks to increasingly larger datasets and more powerful systems, leading to
improvements in the complexity of claims which can be accurately fact-checked.
However, despite these advances, there are still desirable functionalities
missing from the fact-checking pipeline. In this survey, we focus on the
explanation functionality -- that is fact-checking systems providing reasons
for their predictions. We summarize existing methods for explaining the
predictions of fact-checking systems and we explore trends in this topic.
Further, we consider what makes for good explanations in this specific domain
through a comparative analysis of existing fact-checking explanations against
some desirable properties. Finally, we propose further research directions for
generating fact-checking explanations, and describe how these may lead to
improvements in the research area.

本文综述了自动事实检查中解释功能的现有方法，分析了好的解释特性，并描述了相关的进一步研究方向。

可解释的自动事实核查：一项调查

Explainable Automated Fact-Checking: A Survey

Automated fact-checking based on machine learning is a promising approach to
identify false information distributed on the web. In order to achieve
satisfactory performance, machine learning methods require a large corpus with
reliable annotations for the different tasks in the fact-checking process.
Having analyzed existing fact-checking corpora, we found that none of them
meets these criteria in full. They are either too small in size, do not provide
detailed annotations, or are limited to a single domain. Motivated by this gap,
we present a new substantially sized mixed-domain corpus with annotations of
good quality for the core fact-checking tasks: document retrieval, evidence
extraction, stance detection, and claim validation. To aid future corpus
construction, we describe our methodology for corpus creation and annotation,
and demonstrate that it results in substantial inter-annotator agreement. As
baselines for future research, we perform experiments on our corpus with a
number of model architectures that reach high performance in similar problem
settings. Finally, to support the development of future models, we provide a
detailed error analysis for each of the tasks. Our results show that the
realistic, multi-domain setting defined by our data poses new challenges for
the existing models, providing opportunities for considerable improvement by
future systems.

基于机器学习的自动事实核查是识别网络上的虚假信息的一种有希望的方法，针对现有的事实核查语料库规模小、注释不详细、局限于单一领域等问题，本研究提出了一个大规模、多领域的可信事实核查语料库，并进行了模型构建和分析。

自动事实检查中不同任务的丰富注释语料库

A Richly Annotated Corpus for Different Tasks in Automated Fact-Checking

Misinformation such as fake news is one of the big challenges of our society.
Research on automated fact-checking has proposed methods based on supervised
learning, but these approaches do not consider external evidence apart from
labeled training instances. Recent approaches counter this deficit by
considering external sources related to a claim. However, these methods require
substantial feature modeling and rich lexicons. This paper overcomes these
limitations of prior work with an end-to-end model for evidence-aware
credibility assessment of arbitrary textual claims, without any human
intervention. It presents a neural network model that judiciously aggregates
signals from external evidence articles, the language of these articles and the
trustworthiness of their sources. It also derives informative features for
generating user-comprehensible explanations that makes the neural network
predictions transparent to the end-user. Experiments with four datasets and
ablation studies show the strength of our method.

本研究提供了一种端到端模型，该模型可对文字主张进行证据感知的可信度评估，不需要任何人类干预，并通过聚合来自外部证据文章的信号，这些文章的语言以及其来源的可信度来生成用户可理解的解释，从而提高了之前工作的局限性，实验证明其有效性。