This paper presents an end-to-end system for fact extraction and verification using textual and tabular evidence, the performance of which we demonstrate on the FEVEROUS dataset. We experiment with both a multi-task learning paradigm to jointly train a graph attention network for both the task of evidence extraction and veracity prediction, as well as a single objective graph model for solely learning veracity prediction and separate evidence extraction. In both instances, we employ a framework for per-cell linearization of tabular evidence, thus allowing us to treat evidence from tables as sequences. The templates we employ for linearizing tables capture the context as well as the content of table data. We furthermore provide a case study to show the interpretability our approach. Our best performing system achieves a FEVEROUS score of 0.23 and 53% label accuracy on the blind test data.

翻译：本文介绍了利用文字证据和表格证据进行事实提取和核实的端到端系统,我们用这些证据的性能在Feveroous数据集上展示。我们实验了多种任务学习模式,共同培训一个用于证据提取和真实性预测任务的图形关注网络,以及一个仅用于学习真实性预测和单独证据提取的单一客观图表模型。在这两种情况下,我们采用一个框架,将表格证据的每个细胞线性化,从而使我们能够将表格中的证据作为序列处理。我们用于线性化表格的模板既包括表格数据的内容,也包含背景。我们还提供了案例研究,以展示我们的方法的可解释性。我们的最佳运行系统在盲点数据上达到了0.23%和53%的标签准确度。