用验证信号重排序提升多语言科学主张与文献匹配精度

Multilingual scientific claim-source retrieval aims to identify the scientific publication supporting a claim shared on social media. This task is challenging because claims often differ from source publications in terms of language, wording, and level of detail, which weakens the connection between claims and their underlying evidence. In this paper, we present our approach for the CheckThat! 2026 Lab Task 1: Source Retrieval for Scientific Web Claims. We propose a multi-stage retrieval framework for multilingual scientific claim-source retrieval that combines structured claim and source representations with progressive candidate refinement. To address multilingual retrieval challenges, the framework employs bilingual claim representations, metadata-enhanced source representations, and language-specific adaptation of dense retrieval models. Building on this setup, a first-stage retriever generates an initial pool of candidate sources, after which similarity-based re-ranking improves the ranking of highly relevant sources and verification-based re-ranking identifies the candidate source that best supports the claim using verification signals. Our approach achieves an average MRR@5 score of 0.7628 across English, German, and French claims, ranking first on the CheckThat! 2026 leaderboard.