F act D ebug at S em E val-2025 Task 7: Hybrid Retrieval Pipeline for Identifying Previously Fact-Checked Claims Across Multiple Languages (2025)

Abstract: The proliferation of multilingual misinformation demands robust systems for crosslingual fact-checked claim retrieval. This paper addresses SemEval-2025 Shared Task 7, which challenges participants to retrieve fact-checks for social media posts across 14 languages, even when posts and fact-checks are in different languages. We propose a hybrid retrieval pipeline that combines sparse lexical matching (BM25, BGE-m3) and dense semantic retrieval (pretrained and fine-tuned BGE-m3) with dynamic fusion and curriculum-trained rerankers. Our system achieves 67.2% crosslingual and 86.01% monolingual accuracy on the Shared Task MultiClaim dataset.

## introduction nowadays, fact-checking has become crucially important because it helps maintain accuracy and credibility, prevents the spread of misinformation, and ensures informed decision-making by verifying information before dissemination. semeval-2025 task 7 is focused on previously fact-checked claim retrieval (pfcr) @xcite . the task involves ranking a set of factchecked claims according to their relevance to an input claim such as a social media post, with the highest-ranking ones being most pertinent and beneficial for fact-checking. so far only monolingual pfcr has been tackled, when the input claim and the fact-checked claims are in the same language. to address these shortcomings, the semeval-2025 task 7 @xcite has been organized with the multiclaim dataset @xcite to encourage the community to develop a multilingual fact-checking system. the task is divided into two main subtasks: (1) monolingual fact-checking -given a social post, participants must develop systems to identify and retrieve the most relevant fact-checked claim written in the same language as the post; this paper is structured as follows. section 2 discusses existing work on fact-checking. section 3 describes the modified version of the multiclaim dataset that was used in the semeval-2025 task 7. section 4 introduces the evaluation metrics. section 5 describes the proposed hybrid retrieval pipeline. section 6 reports the results of the applied approaches. our contribution can be summarized as follows. we introduce a lightweight yet effective factchecking hybrid retrieval system that incorporates multistage fine-tuning components. this technique efficiently aligns multilingual social media posts with a multilingual fact-check corpus. ## related work the information retrieval component is an essential part of any qa system, not only because it improves qa performance, but also because it enhances fact-checking @xcite . pfcr task is time-consuming for professional factcheckers and information retrieval methods can speed up the process. multilingual pfcr is an even more complicated version for humans because it requires a deep understanding of multiple languages. multilingual information search can facilitate the fact-checking between different languages. for pfcr, traditional methods of information retrieval can be utilized. @xcite effectively employed the bm25 method to identify fake news. various text embedding techniques have been used to improve the retrieval process, allowing more nuanced comparisons between claims, and also techniques such as reranking are used to combine multiple methods, enhancing the efficiency and accuracy of claim retrieval @xcite . similar ideas are used to retrieve claims for comparative questions @xcite . in addition, some approaches enhance the results by incorporating visual data from images, using abstractive summarization, or identifying key sentences. xlm-roberta based bge-m3 provides more contextually rich and semantically accurate representations of text, ultimately leading to more relevant and precise search results. it can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multivector retrieval, and sparse retrieval utilizing the transformer ability to integrate several tasks @xcite . research emphasizes the importance of context in detecting previously fact-checked claims, especially in political debates or documents @xcite . some studies focus on detecting claims within entire documents, aiming to rank sentences based on their verifiability using previously fact-checked claims @xcite future research will likely focus on improving the efficiency and accuracy of these systems, particulary in low-resource languges and complex contextual scenarios. ## dataset the competition organizers represented a modified version of the multiclaim dataset. the original multiclaim dataset consists of 28k posts in 27 languages on social media, 206k fact checks in 39 languages performed by professional fact checkers, and 31k connections between the two groups. each connection consists of a post and a fact-check reviewing the claim made in the post. the main difference between the modified version presented in the competition and the original one is that the modified version contains fewer languages ( 14 ), but contains more fact checks (272k) and few more posts. in the competition a modified mul-ticlaim dataset was used. the entire dataset is split into three sections: training (comprising 153k fact-checks in 8 languages, 4,972 cross-lingual and 1,7016 monolingual posts), testing (featuring 272,447 fact-checks in 12 languages, 4,000 crosslingual and 4,276 monolingual posts) and development (consisting of the same fact-checks as training, 552 cross-lingual, and 1,891 monolingual posts). there are also 25,743 connections between posts and fact-checks for training and development parts. ## evaluation to evaluate retrieval in shared task 7, we use success-at-10 (s@10) as a quality measure for both monolingual and cross-lingual subtasks. this is because we depend on the retrieval module to capture as much relevant information as possible. ## proposed approach our rather classical retrieval pipeline combines sparse and dense retrieval paradigms with fusion and reranking to address cross-lingual and monolingual fact-checking tasks. the architecture consists of four stages: (1) sparse vector encoding for lexical matching, (2) dense vector encoding for semantic alignment, (3) fusion to merge multi-perspective results, and (4) reranking to refine relevance ordering. this pipeline was used successfully for the retrieval in the specific domain @xcite .