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Found 5 papers, 2 papers with code
EXPLAIN, EDIT, GENERATE: Rationale-Sensitive Counterfactual Data Augmentation for Multi-hop Fact Verification
Experimental results show that the proposed approach outperforms the SOTA baselines and can generate linguistically diverse counterfactual data without disrupting their logical relationships.
Explainable Topic-Enhanced Argument Mining from Heterogeneous Sources
Specifically, with the use of the neural topic model and the language model, the target information is augmented by explainable topic representations.
Consistent Multi-Granular Rationale Extraction for Explainable Multi-hop Fact Verification
The success of deep learning models on multi-hop fact verification has prompted researchers to understand the behavior behind their veracity.
Exploring Faithful Rationale for Multi-hop Fact Verification via Salience-Aware Graph Learning
In specific, GCN is utilized to incorporate the topological interaction information among multiple pieces of evidence for learning evidence representation.
Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verification
To alleviate the above issues, we propose a novel topic-aware evidence reasoning and stance-aware aggregation model for more accurate fact verification, with the following four key properties: 1) checking topical consistency between the claim and evidence; 2) maintaining topical coherence among multiple pieces of evidence; 3) ensuring semantic similarity between the global topic information and the semantic representation of evidence; 4) aggregating evidence based on their implicit stances to the claim.
