This 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.