Fact checking aims to predict claim veracity by reasoning over multiple
evidence pieces. It usually involves evidence retrieval and veracity reasoning.
In this paper, we focus on the latter, reasoning over unstructured text and
structured table information. Previous works have primarily relied on
fine-tuning pretrained language models or training homogeneous-graph-based
models. Despite their effectiveness, we argue that they fail to explore the
rich semantic information underlying the evidence with different structures. To
address this, we propose a novel word-level Heterogeneous-graph-based model for
Fact Checking over unstructured and structured information, namely HeterFC. Our
approach leverages a heterogeneous evidence graph, with words as nodes and
thoughtfully designed edges representing different evidence properties. We
perform information propagation via a relational graph neural network,
facilitating interactions between claims and evidence. An attention-based
method is utilized to integrate information, combined with a language model for
generating predictions. We introduce a multitask loss function to account for
potential inaccuracies in evidence retrieval. Comprehensive experiments on the
large fact checking dataset FEVEROUS demonstrate the effectiveness of HeterFC.
Code will be released at: this https URL

我们提出了一种用于事实检查的新型基于异构图的模型，名为 HeterFC，它通过一个关系图神经网络进行信息传播，在声明和证据之间促进交互，并结合语言模型生成预测。我们引入了一个多任务损失函数来考虑证据检索的潜在不准确性。综合实验证明了 HeterFC 的有效性。