H oward U niversity- AI 4 PC at S em E val-2025 Task 7: Crosslingual Fact-Checked Claim Retrieval-Combining Zero-Shot Claim Extraction and KNN -Based Classification for Multilingual Claim Matching (2025)

Abstract: SemEval Task 7 introduced a dataset for multilingual and cross-lingual fact checking. We propose a system that leverages similarity matching, KNN, zero-shot classification and summarization to retrieve fact-checks for social media posts across multiple languages. Our approach achieves performance within the expected range, aligning with baseline results. Although competitive, the findings highlight the potential and challenges of zero-shot methods, providing a foundation for future research in cross-lingual information verification.

## introduction the rapid spread of misinformation on social media platforms has highlighted the urgent need for automated fact-checking systems that can operate across multiple languages. multilingual and cross lingual fact checking pose significant challenges, as they require systems to verify claims in diverse linguistic contexts while maintaining high accuracy and scalability @xcite . to address these challenges, semeval task 7 introduced a benchmark dataset designed to evaluate the performance of such systems, providing a platform for advancing research in this domain @xcite . our proposed approach to cross lingual fact checking leverages zero-shot classification and summarization techniques. using zero-shot methods, our system retrieves fact checks associated with social media posts across multiple languages without requiring language-specific training data. this strategy enables the system to bridge the gap between high-resource and low-resource languages, promoting more equitable access to fact-checking tools @xcite . the zero-shot framework allows the system to generalize across languages, making it scalable and adaptable to diverse linguistic contexts. our results demonstrate that the proposed system achieves performance within the expected range, aligning with baseline results reported in prior work. although the results are competitive, they also reveal the inherent challenges of zero-shot methods, particularly in claim classification tasks. these findings validate the feasibility of zero-shot approaches for cross lingual fact-checking and provide a foundation for future research. to support reproducibility and further research, we have released the code for our system. the code can be found in appendix a. ## system overview our cross lingual claim matching system is composed of three main components: text translation, knowledge base creation and fact-check retrieval. ## results the performance of our system was evaluated using the success@10 (s@10) metric, which measures whether at least one associated fact check is successfully retrieved within the top 10 results for a given social media post. ## limitations and future work although our system combines knn with zeroshot classification to reduce the search space, it remains computationally intensive. as highlighted in the results, simple knn outperformed zero-shot classification, underscoring the limitations of zeroshot methods for claim matching in this context. additionally, the translation process may lead to the loss of certain linguistic nuances and information @xcite , further contributing to the system's reduced effectiveness. looking ahead, we aim to address these limitations by exploring multilingual embedding models and large language models (llms) that are better suited for cross-lingual tasks @xcite . we also plan to finetune models specifically for claim extraction and claim matching to improve accuracy and efficiency. these refinements are expected to enhance the system's performance and scalability in multilingual fact-checking scenarios. ## conclusion in this paper, we presented a system for cross lingual fact-checked claim retrieval, addressing the challenges posed by semeval task 7. our approach leveraged a combination knn search and zero-shot classification. the results demonstrated that simple similarity search methods, such as knn, outperformed zero-shot classification for the claim matching task, achieving an average success@10 score of 0.59225 and securing the 21st position on the leaderboard. while this performance is competitive, it highlights the potential for further refinement, particularly in improving the precision of claim classification. future work will focus on optimizing the classification process, and exploring more robust embedding techniques.