This study proposes Semantic-Infused SElective Graph Reasoning (SISER) for fact verification, which newly presents semantic-level graph reasoning and injects its reasoning-enhanced representation into other types of graph-based and sequence-based reasoning methods. SISER combines three reasoning types: 1) semantic-level graph reasoning, which uses a semantic graph from evidence sentences, whose nodes are elements of a triple – <Subject, Verb, Object>, 2) “semantic-infused” sentence-level “selective” graph reasoning, which combine semantic-level and sentence-level representations and perform graph reasoning in a selective manner using the node selection mechanism, and 3) sequence reasoning, which concatenates all evidence sentences and performs attention-based reasoning. Experiment results on a large-scale dataset for Fact Extraction and VERification (FEVER) show that SISER outperforms the previous graph-based approaches and achieves state-of-the-art performance.

This paper presents our contributions to the SemEval-2020 Task 4 Commonsense Validation and Explanation (ComVE) and includes the experimental results of the two Subtasks B and C of the SemEval-2020 Task 4. Our systems rely on pre-trained language models, i.e., BERT (including its variants) and UniLM, and rank 10th and 7th among 27 and 17 systems on Subtasks B and C, respectively. We analyze the commonsense ability of the existing pretrained language models by testing them on the SemEval-2020 Task 4 ComVE dataset, specifically for Subtasks B and C, the explanation subtasks with multi-choice and sentence generation, respectively.