Word2winners at SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval
This addresses fact-checking retrieval for social media posts across multiple languages, but it is incremental as it builds on existing models with fine-tuning and translation.
The paper tackled the problem of retrieving relevant fact-checks for input claims from a multilingual dataset, achieving 85% accuracy on crosslingual data and 92% on monolingual data.
This paper describes our system for SemEval 2025 Task 7: Previously Fact-Checked Claim Retrieval. The task requires retrieving relevant fact-checks for a given input claim from the extensive, multilingual MultiClaim dataset, which comprises social media posts and fact-checks in several languages. To address this challenge, we first evaluated zero-shot performance using state-of-the-art English and multilingual retrieval models and then fine-tuned the most promising systems, leveraging machine translation to enhance crosslingual retrieval. Our best model achieved an accuracy of 85% on crosslingual data and 92% on monolingual data.