  Information verification is quite a challenging task, this is because many
times verifying a claim can require picking pieces of information from multiple
pieces of evidence which can have a hierarchy of complex semantic relations.
Previously a lot of researchers have mainly focused on simply concatenating
multiple evidence sentences to accept or reject claims. These approaches are
limited as evidence can contain hierarchical information and dependencies. In
this research, we aim to verify facts based on evidence selected from a list of
articles taken from Wikipedia. Pretrained language models such as XLNET are
used to generate meaningful representations and graph-based attention and
convolutions are used in such a way that the system requires little additional
training to learn to verify facts.
