fact check AI at SemEval-2025 Task 7: Multilingual and Crosslingual Fact-checked Claim Retrieval

AbstractThe SemEval-2025 Task 7 on Multilingualand Crosslingual Fact-checked Claim Retrievalfocuses on retrieving relevant Fact-checkedclaims for social media Posts across multiplelanguages. This task is particularly challengingdue to linguistic barriers and the vast numberof languages Fact-checkers must consider.In this work, I approach the problem as aLearning-to-Rank task and solve it using abi-encoder-based model, fine-tuned on a pre-trained transformer optimized for sentence sim-ilarity. For the monolingual task, training wasperformed in both the source languages andtheir English translations. For cross-lingualretrieval, the training relied on English transla-tions.Most fine-tuned models have fewer than 500Mparameters, and the training was carried outefficiently using kaggle T4 GPUs with paral-lelization. Despite this lightweight setup, ourapproach achieved 92% Success@10 for mul-tilingual retrieval and 80% Success@10 forcross-lingual retrieval, securing 5th place inthe cross-lingual track and 10th place in themultilingual setting.