Automatic fact verification has become an increasingly popular topic in recent years and among datasets the Fact Extraction and VERification (FEVER) dataset is one of the most popular. In this work we present BEVERS, a tuned baseline system for the FEVER dataset. Our pipeline uses standard approaches for document retrieval, sentence selection, and final claim classification, however, we spend considerable effort ensuring optimal performance for each component. The results are that BEVERS achieves the highest FEVER score and label accuracy among all systems, published or unpublished. We also apply this pipeline to another fact verification dataset, Scifact, and achieve the highest label accuracy among all systems on that dataset as well. We also make our full code available.

翻译：近年来，自动事实验证已成为一个日益热门的研究课题，其中事实提取与验证（FEVER）数据集是最受欢迎的数据集之一。本文提出BEVERS——一个针对FEVER数据集调优的基线系统。我们的流程采用文档检索、句子选取和最终声明分类的标准方法，但通过大量工作确保每个组件达到最优性能。结果显示，在所有已发表或未发表的系统中，BEVERS取得了最高的FEVER分数和标签准确率。我们还将此流程应用于另一个事实验证数据集Scifact，并在该数据集上也实现了所有系统中的最高标签准确率。此外，我们已公开完整代码。