| Popis: |
Fact-checking is a task to capture the relation between a claim and evidence (premise) to decide this claim’s truth. Detecting the factuality of claim, as in fake news, depending only on news knowledge, e.g., evidence text, is generally inadequate since fake news is intentionally written to mislead readers. Most of the previous models on this task rely on claim and evidence argument as input for their model, where sometimes the systems fail to detect the relation, particularly for ambiguate information. This study aims to improve fact-checking task by incorporating warrant as a bridge between the claim and the evidence, illustrating why this evidence supports this claim, i.e., If the warrant links between the claim and the evidence then the relation is supporting, if not it is either irrelevant or attacking, so warrants are applicable only for supporting the claim. To solve the problem of gap semantic between claim evidence pair, A model that can detect the relation based on existing extracted warrants from structured data is developed. For warrant selection, knowledge-based prediction and style-based prediction models are merged to capture more helpful information to infer which warrant represents the best bridges between claim and evidence. Picking a reasonable warrant can help alleviate the evidence ambiguity problem if the proper relation cannot be detected. Experimental results show that incorporating the best warrant to fact-checking model improves the performance of fact-checking. |
