The task of fact-checking deals with assessing the veracity of factual claims based on credible evidence and background knowledge. In particular, scientific fact-checking is the variation of the task concerned with verifying claims rooted in scientific knowledge. This task has received significant attention due to the growing importance of scientific and health discussions on online platforms. Automated scientific fact-checking methods based on NLP can help combat the spread of misinformation, assist researchers in knowledge discovery, and help individuals understand new scientific breakthroughs. In this paper, we present a comprehensive survey of existing research in this emerging field and its related tasks. We provide a task description, discuss the construction process of existing datasets, and analyze proposed models and approaches. Based on our findings, we identify intriguing challenges and outline potential future directions to advance the field.

翻译：事实核查任务旨在基于可靠证据与背景知识评估事实性主张的真实性。其中，科学事实核查是专注于验证植根于科学知识的主张的特定任务变体。随着科学议题与健康讨论在网络平台上的重要性日益凸显，该任务已获得广泛关注。基于自然语言处理的自动化科学事实核查方法，可助力打击虚假信息传播、辅助研究人员进行知识发现，并帮助公众理解新的科学突破。本文对这一新兴领域及其相关任务的研究现状进行了全面综述。我们提供了任务定义，探讨了现有数据集的构建过程，并分析了已提出的模型与方法。基于研究结果，我们识别了具有挑战性的关键问题，并提出了推动该领域发展的潜在未来方向。