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.

本文提出了一个端到端的事实提取和验证系统，使用文本和表格证据，探讨了多任务学习图注意力网络同时训练证据提取和真实性预测任务，单独学习真实性预测和分离式证据提取的操作，并通过表格线性化模板捕捉表格数据的上下文和内容。最佳模型在盲测试数据中获得 FEVEROUS 得分 0.23 和 53% 的标签准确性。