三阶段检索框架提升事实核查声明召回准确率
QUST_NLP at SemEval-2025 Task 7: A Three-Stage Retrieval Framework for Monolingual and Crosslingual Fact-Checked Claim Retrieval
- 分三阶段：候选检索、多模型重排序、加权投票融合
- 在单语赛道获第5名，跨语言赛道第7名
- 适合需要高精度事实核查的系统开发者
本文介绍QUST_NLP团队参与SemEval-2025任务7的成果。我们提出一种专为事实核查声明检索设计的三阶段检索框架。首先评估多种检索模型，选取表现最优者进行候选检索；其次，采用多个重排序模型对候选结果进行优化，每个模型保留前10项；最后通过加权投票确定最终结果。该方法在单语赛道中取得第5名，在跨语言赛道中获得第7名。相关系统代码已公开于https://github.com/warmth27/SemEval2025_Task7。
原文摘要 · Abstract (English)
This paper describes the participation of QUST_NLP in the SemEval-2025 Task 7. We propose a three-stage retrieval framework specifically designed for fact-checked claim retrieval. Initially, we evaluate the performance of several retrieval models and select the one that yields the best results for candidate retrieval. Next, we employ multiple re-ranking models to enhance the candidate results, with each model selecting the Top-10 outcomes. In the final stage, we utilize weighted voting to determine the final retrieval outcomes. Our approach achieved 5th place in the monolingual track and 7th place in the crosslingual track. We release our system code at: https://github.com/warmth27/SemEval2025_Task7
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