  Fact verification (FV) is a challenging task which requires to retrieve
relevant evidence from plain text and use the evidence to verify given claims.
Many claims require to simultaneously integrate and reason over several pieces
of evidence for verification. However, previous work employs simple models to
extract information from evidence without letting evidence communicate with
each other, e.g., merely concatenate the evidence for processing. Therefore,
these methods are unable to grasp sufficient relational and logical information
among the evidence. To alleviate this issue, we propose a graph-based evidence
aggregating and reasoning (GEAR) framework which enables information to
transfer on a fully-connected evidence graph and then utilizes different
aggregators to collect multi-evidence information. We further employ BERT, an
effective pre-trained language representation model, to improve the
performance. Experimental results on a large-scale benchmark dataset FEVER have
demonstrated that GEAR could leverage multi-evidence information for FV and
thus achieves the promising result with a test FEVER score of 67.10%. Our code
is available at https://github.com/thunlp/GEAR.
