SISER : Semantic-Infused Selective Graph Reasoning for Fact Verification (2022)

Abstract: This study proposes S emantic- I nfused SE lective Graph R easoning (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.

## introduction an ever-increasing number of unconfirmed false or misleading information spread on various social media platforms has motivated the verification of textual information, referred to as fact verification. fever @xcite presented a large dataset for fact verification, initiating a shared task that aims to automatically classify a human-generated claim into 'supported', 'refuted', or 'not enough info' based on retrieved evidence sentences from wikipedia 1 . claim verification, the final step of fact verification, is viewed as a task of natural language inference (nli) @xcite . specif-ically, the nli task for claim verification is formulated as the set-to-sentence entailment of inferring whether a claim (as the hypothesis) is logically "entailed" from a set of retrieved evidence sentences (as the premise). recently, graph reasoning for claim verification has been extensively explored @xcite @xcite , which creates a graph whose nodes are semantic units extracted from a set of evidence sentences or a claim, and applies graph neural networks (gnns) such as @xcite to infer the entailment relationship. however, graph reasoning may be somehow restricted to unit-biased reasoning, when relying on a single type of semantic unit for nodes of a graph, such as sentences, entities, or words, meaning that the semantic interaction between claim and evidence is restricted to a single graph type and does not go beyond the coverage of the "given" semantic units. in addition, graph reasoning may suffer from oversmoothing inherited from gnns @xcite @xcite , likely causing all node representations to converge to a stationary point at the extreme, as reported by @xcite . to address these limitations of graph reasoning, this study proposes siser -semantic-infused selective graph reasoning) for fact verification by extensively exploiting additional semantic units for graph reasoning and integrating semantic-level reasoning with sequence reasoning and "selective" graph reasoning. siser combines the following three types of reasoning: our contributions are summarized as follows: 1) we propose siser, which consists primarily of semantic-level reasoning and semantic-infused selective graph reasoning using the node selection mechanism for fact verification; 2) we present the initial work of adopting prompt-based fine-tuning for claim verification; 3) the proposed siser shows state-of-the-art performance in the fever dataset. ## proposed approach figure 1 shows the overall neural architecture of the proposed siser model, which combines three types of reasoning: i.e., semantic-level graph reasoning; semantic-infused sentence-level selective graph reasoning; and sequence reasoning. this section presents details of the three reasoning methods. ## conclusion in this paper, we propose siser for fact verification, which combines three types of reasoning (i.e., semantic-level graph reasoning, semanticinfused sentence-level selective graph reasoning, and sequence reasoning) by addressing two potential limitations of graph reasoning -the "unitbiased reasoning" and the "over-smoothing" problems. the experimental results obtained using the fever dataset showed that the proposed siser outperformed other graph-based approaches and achieved state-of-the-art performances in both the development and test sets. in future work, we would like to incorporate semantic-level and semantic-fused graph reasoning into evidence retrieval and explore the joint learning framework of evidence retrieval and claim verification in a multi-task learning setting.