2025
ACL
ACL 2025
DKE-Research at SemEval-2025 Task 7: A Unified Multilingual Framework for Cross-Lingual and Monolingual Retrieval with Efficient Language-specific Adaptation
Abstract
AbstractThis paper presents a unified framework for fact-checked claim retrieval, integrating contrastive learning with an in-batch multiple negative ranking loss and a conflict-aware batch sampler to enhance query-document alignment across languages. Additionally, we introduce language-specific adapters for efficient fine-tuning, enabling adaptation to previously unseen languages.
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Interdisciplinary Bridge
— Machine Learning and Natural Language Processing
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Keyword Pioneer
— fact-checked claim retrieval
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Cross-Pollinator
— Artificial Intelligence, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio
Authors
Topics
Artificial Intelligence > Learning Paradigms > Transfer Learning
Machine Learning > Learning Types > Contrastive Learning
Natural Language Processing > Applications > Information Retrieval
Natural Language Processing > Resources & Methods > Multilingual NLP
Machine Learning > Learning Types > Transfer Learning