  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.
