QUST _ NLP at S em E val-2025 Task 7: A Three-Stage Retrieval Framework for Monolingual and Crosslingual Fact-Checked Claim Retrieval (2025)

Abstract: This paper describes the participation of team 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.

## introduction semeval-2025 shared task 7 focuses on the retrieval of monolingual and crosslingual factchecked claims, aiming to tackle the global challenge of misinformation spread @xcite . we engaged in two tracks of the semeval-2025 shared task 7: monolingual and crosslingual. the monolingual track demands methods capable of retrieving the relationship between social media posts and fact-checked claims within the same linguistic environment. this task presents challenges such as noise arising from the large volume of data and difficulties related to the imbalance of language resources @xcite . the crosslingual track requires methods that can retrieve factchecked claims related to social media posts regardless of whether the language of the post matches the language of the related fact-checked claim. the primary challenge in crosslingual retrieval lies in translation inconsistencies, particulaedrly for lowresource languages (qi et al., 2023; magueresse * corresponding author @xcite . the absence of high-quality translation tools exacerbates the complexity of achieving accurate crosslingual semantic alignment. to tackle the aforementioned challenges, we propose a three-stage retrieval framework. initially, we evaluate and employ several pre-trained language models for preliminary retrieval of candidate results @xcite , thereby mitigating the noise caused by the large data volume and alleviating the adverse effects of language resource imbalance. subsequently, a re-ranking model is applied to refine the ranking of the candidate results, enhancing the position of fact-checked claims most relevant to the social media posts. for the crosslingual retrieval task, we utilize machinetranslated data for preliminary retrieval, followed by ranking the results using a re-ranking model fine-tuned with english data. finally, a weighted voting strategy is employed to combine the outputs from multiple re-ranking models, further enhancing the system's accuracy. our approach achieved 5th place in the monolingual track and 7th place in the crosslingual track, thereby validating its effectiveness and feasibility in addressing the aforementioned challenges. ## system description our approach utilizes a three-stage retrieval framework: retrieval stage, re-ranking stage, and weighted voting stage. this staged design excels at balancing retrieval efficiency and accuracy, making it particularly suitable for handling large-scale datasets. by generating candidate results during the initial retrieval stage, fine-tuning them during the re-ranking phase, and finally aggregating predictions from multiple models in the weighted voting stage, we are able to obtain the final solution. the detailed process is shown in figure 1 . ## conclusion and limitation this paper introduces a monolingual and crosslingual fact-checked claim retrieval method utilizing a three-stage retrieval framework. by integrating retrieval models, re-ranking models, and weighted voting, we effectively address challenges such as data noise and imbalanced language resources. our findings suggest that employing a mixed input strategy markedly enhances retrieval performance, while fine-tuning further optimizes re-ranking efficacy. our method achieved 5th place in the monolingual track and 7th place in the crosslingual track. we acknowledge that our method has limitations in terms of translation consistency and quality. future work will focus on enhancing translation quality and refine model fine-tuning strategies to overcome these challenges.