TL;DR
This paper presents a three-stage retrieval framework for fact-checked claim retrieval in both monolingual and crosslingual contexts, combining model selection, re-ranking, and voting to improve accuracy.
Contribution
The authors introduce a novel three-stage retrieval framework tailored for fact-checked claim retrieval, demonstrating competitive results in SemEval-2025 Task 7.
Findings
Achieved 5th place in monolingual track
Achieved 7th place in crosslingual track
Effective combination of retrieval, re-ranking, and voting improves results
Abstract
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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