关键词: 文本事实验证, 图注意力网络, 核函数, [N]-gram, 卷积神经网络

Abstract:

Fact Verification（FV） requires to retrieve evidences from large-scale corpus to verify claims. Existing methods simply concatenate evidences and compare the cosine similarity between them, while ignoring the relation between evidences and the similarity on different levels. This paper presents the new model Graph-aware Hierarchical Attention Networks（GHAN）. It can firstly retrieve evidences from Wikipedia through BERT（Bidirectional Encoder Representation from Transformers） and leverages convolutional neural network to extract features with different lengths. The translation matrix is consisted by these features with different granularities. Furthermore, this paper builds a graph among evidences in order to take into account both token-level and sentence-level information. And the kernel attention mechanism contributes to information propagation and verification. Experimental results on a large-scale benchmark dataset FEVER demonstrate that GHAN surpasses other published models based on BERT.

Key words: fact verification on text, graph attention network, kernel function;[N]-gram, convolutional neural network