In natural language processing, fact verification is a very challenging task, which requires retrieving multiple evidence sentences from a reliable corpus to verify the authenticity of a claim. Although most of the current deep learning methods use the attention mechanism for fact verification, they have not considered imposing attentional constraints on important related words in the claim and evidence sentences, resulting in inaccurate attention for some irrelevant words. In this paper, we propose a syntactic evidence network (SENet) model which incorporates entity keywords, syntactic information and sentence attention for fact verification. The SENet model extracts entity keywords from claim and evidence sentences, and uses a pre-trained syntactic dependency parser to extract the corresponding syntactic sentence structures and incorporates the extracted syntactic information into the attention mechanism for language-driven word representation. In addition, the sentence attention mechanism is applied to obtain a richer semantic representation. We have conducted experiments on the FEVER and UKP Snopes datasets for performance evaluation. Our SENet model has achieved 78.69% in Label Accuracy and 75.63% in FEVER Score on the FEVER dataset. In addition, our SENet model also has achieved 65.0% in precision and 61.2% in macro F1 on the UKP Snopes dataset. The experimental results have shown that our proposed SENet model has outperformed the baseline models and achieved the state-of-the-art performance for fact verification.

Fact verification is a challenging task that requires the retrieval of multiple pieces of evidence from a reliable corpus for verifying the truthfulness of a claim. Although the current methods have achieved satisfactory performance, they still suffer from one or more of the following three problems: (1) unable to extract sufficient contextual information from the evidence sentences; (2) containing redundant evidence information and (3) incapable of capturing the interaction between claim and evidence. To tackle the problems, we propose an evidence fusion network called EvidenceNet. The proposed EvidenceNet model captures global contextual information from various levels of evidence information for deep understanding. Moreover, a gating mechanism is designed to filter out redundant information in evidence. In addition, a symmetrical interaction attention mechanism is also proposed for identifying the interaction between claim and evidence. We conduct extensive experiments based on the FEVER dataset. The experimental results have shown that the proposed EvidenceNet model outperforms the current fact verification methods and achieves the state-of-the-art performance.