Claim2Vec: Embedding Fact-Check Claims for Multilingual Similarity and Clustering

Claim2Vec is a novel multilingual embedding model designed to represent fact-check claims as vectors for improved semantic understanding. It addresses the challenge of claim clustering for misinformation by leveraging contrastive learning on similar multilingual claim pairs, significantly enhancing performance.