WikiCheck：基于维基百科的端到端开源自动事实检查API
WikiCheck: An end-to-end open source Automatic Fact-Checking API based on Wikipedia
摘要：
随着假新闻和虚假信息的增长，NLP社区一直在努力帮助人类进行事实核查。然而，大多数学术研究都集中在模型准确性上，而没有关注资源效率，这在现实生活中至关重要。在这项工作中，我们回顾了自动事实检查的最先进的数据集和解决方案，并测试了它们在生产环境中的适用性。我们发现这些模型中存在过拟合问题，并提出了一种数据过滤方法，提高了模型的性能和泛化能力。然后，我们设计了一个无监督微调的掩蔽语言模型，以提高其准确性与维基百科。我们还提出了一种新的查询增强方法，以提高使用维基百科搜索API的证据发现。最后，我们提出了一个新的事实检查系统，\textit{WikiCheck} API，自动执行的事实验证过程的基础上，维基百科的知识库。它在准确性方面与SOTA解决方案相当，并且可以用于低内存CPU实例。
英文摘要：
With the growth of fake news and disinformation, the NLP community has been working to assist humans in fact-checking. However, most academic research has focused on model accuracy without paying attention to resource efficiency, which is crucial in real-life scenarios. In this work, we review the State-of-the-Art datasets and solutions for Automatic Fact-checking and test their applicability in production environments. We discover overfitting issues in those models, and we propose a data filtering method that improves the model's performance and generalization. Then, we design an unsupervised fine-tuning of the Masked Language models to improve its accuracy working with Wikipedia. We also propose a novel query enhancing method to improve evidence discovery using the Wikipedia Search API. Finally, we present a new fact-checking system, the \textit{WikiCheck} API that automatically performs a facts validation process based on the Wikipedia knowledge base. It is comparable to SOTA solutions in terms of accuracy and can be used on low-memory CPU instances.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
