UniBuc-AE at SemEval-2025 Task 7: Training Text Embedding Models for Multilingual and Crosslingual Fact-Checked Claim Retrieval

AbstractThis paper describes our approach to the SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval on both the monolingual and crosslingual tracks. Our training methodology for text embedding models combines contrastive pre-training and hard negatives mining in order to fine-tune models from the E5 family. Additionally, we introduce a novel approach for merging the results from multiple models by finding the best majority vote weighted configuration for each subtask using the validation dataset. Our team ranked 6th in the monolingual track scoring a 0.934 S@10 averaged over all languages and achieved a 0.79 S@10 on the crosslingual task, ranking 8th in this track.