Abstract
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 multievidence 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