fact check AI at SemEval-2025 Task 7: Multilingual and Crosslingual Fact-checked Claim Retrieval
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Abstract (arXiv)
SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval is approached as a Learning-to-Rank task using a bi-encoder model fine-tuned from a pre-trained transformer optimized for sentence similarity. Training used both the source languages and their English translations for multilingual retrieval and only English translations for cross-lingual retrieval. Using lightweight models with fewer than 500M parameters and training on Kaggle T4 GPUs, the method achieved 92% Success@10 in multilingual and 80% Success@10 in 5th in crosslingual and 10th in multilingual tracks.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 8 references that are in the map. First posted on arXiv 5 Aug 2025.
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References · 5
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
- 2017Efficient Natural Language Response Suggestion for Smart Replycited by 228
- 2023Multilingual Previously Fact-Checked Claim Retrievalcited by 32
- 2025SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrievalcited by 11
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