BEVERS: A General, Simple, and Performant Framework for Automatic Fact Verification
自动事实验证近年来变得越来越受欢迎，在数据集方面，事实提取和验证(FEVER)数据集是最受欢迎的之一。在本工作中，我们介绍了BEVERS，一个针对FEVER数据集的优化基准系统。我们的管道使用标准的方法来检索文档、选择句子以及最终声明分类，但是我们需要投入相当大的努力确保每个组件的最佳表现。结果是，BEvers在所有系统中公开或私有情况下获得FEVER得分和标签准确性最高的结果。我们还将此管道应用于另一个事实验证数据集Scifact，并在所有系统中在该数据集上获得最高的标签准确性。我们同时也提供了我们的完整代码。
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 The results are that BEVERS achieves the highest FEVER score and label accuracy among all systems, published or unpublished.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.
https://arxiv.org/abs/2303.16974
https://arxiv.org/pdf/2303.16974