The Graph Reasoning Approach Based on the Dynamic Knowledge Auxiliary for Complex Fact Verification

Electronics

Yongyue Wang¹  Chunhe Xia¹  Chengxiang Si²  Tianbo Wang³  Chongyu Zhang⁴ 

[1] Beijing Key Laboratory of Network Technology, School of Computer Science and Engineering, Beihang University, Beijing 100191, China;National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing 100029, China;School of Cyber Science and Technology, Beihang University, Beijing 100191, China;Talent Introduction Department, JD Group, Beijing 100176, China;

关键词: fact verification;    knowledge enhanced;    pre-training model;    graph neural network;    cognitive reasoning;   

DOI  :  10.3390/electronics9091472

来源: DOAJ

【 摘 要 】

Complex fact verification (FV) requires fusing scattered sequences and performing multi-hop reasoning over these composed sequences. Recently, by employing some FV models, knowledge is obtained from context to support the reasoning process based on pretrained models (e.g., BERT, XLNET), and this model outperforms previous out-of-the-art FV models. In practice, however, the limited training data cannot provide enough background knowledge for FV tasks. Once the background knowledge changed, the pretrained models’ parameters cannot be updated. Additionally, noise against common sense cannot be accurately filtered out due to the lack of necessary knowledge, which may have a negative impact on the reasoning progress. Furthermore, existing models often wrongly label the given claims as ‘not enough information’ due to the lack of necessary conceptual relationship between pieces of evidence. In the present study, a Dynamic Knowledge Auxiliary Graph Reasoning (DKAR) approach is proposed for incorporating external background knowledge in the current FV model, which explicitly identifies and fills the knowledge gaps between provided sources and the given claims, to enhance the reasoning ability of graph neural networks. Experiments show that DKAR put forward in this study can be combined with specific and discriminative knowledge to guide the FV system to successfully overcome the knowledge-gap challenges and achieve improvement in FV tasks. Furthermore, DKAR is adopted to complete the FV task on the Fake NewsNet dataset, showing outstanding advantages in a small sample and heterogeneous web text source.

【 授权许可】

Unknown