ipezo TU at S em E val-2025 Task 7: Hybrid Ensemble Retrieval for Multilingual Fact-Checking (2025)

Abstract: Fact-check retrieval plays a crucial role in combating misinformation by ensuring that claims are accurately matched with relevant fact-checks. In this work, we present a hybrid retrieval pipeline that integrates lexical and semantic retrieval models, leveraging their complementary strengths. We evaluate different retrieval and reranking strategies, demonstrating that hybrid ensembling consistently outperforms individual models, while reranking provides only marginal improvements.

## introduction social media has transformed the way information is shared by enabling instant, unfiltered access to news and various perspectives. unlike traditional media, it allows anyone to publish content without verification, making it more difficult to distinguish between true and misleading claims @xcite . the traditional approach of manual fact-checking has proved its reliability in providing high-quality results, however, it lacks the scalability to address the volume and speed of online information spread. the amount of data is rapidly growing as the same misinformation is often spread across different platforms while slightly altered in format, detail, length, or even language. it is often the case that the users are unaware of the veracity of the claims, especially in non-english contexts where fact-checking resources may be limited or less accessible @xcite . this highlights the need to develop and automate fact-checking systems to help maintain the accuracy and reliability of information shared online @xcite . semeval-2025 shared task 7 @xcite ) tackles the challenge of multilingual and crosslingual previously fact-checked claim retrieval (pfcr) @xcite , a critical task in combating misinformation across languages. the task is divided into two subtasks: monolingual and crosslingual retrieval. in the monolingual subtask, the search space is restricted to fact-checked claims in the same language as the query claim. in contrast, the crosslingual subtask allows retrieval across multiple languages, enabling corresponding fact-checks in any language to be retrieved for a given query. the monolingual subtask includes data for arabic, english, french, german, malay, portuguese, spanish, and thai, with polish and turkish added to the test set. this paper explores the effectiveness of hybrid retrieval architectures for monolingual and crosslingual pfcr. we focus on zero-shot retrieval @xcite , avoiding finetuning to ensure general applicability across diverse topics, languages, and platforms. by leveraging pre-trained models, our approach maintains competitive performance with minimal resource demands, demonstrating their effectiveness in multilingual settings without task-specific adaptations. in both subtasks, we achieved our best results with a retriever ensembler, ranking 8th out of 28 teams in the monolingual and 12th out of 29 teams in the crosslingual task with over 177 participants and 1400 submissions. the remainder of this work is structured as follows: we introduce the task and give a factchecking pipeline overview in sec. 2. sec. 3 describes the modules of our system, while sec. 4 presents key experiments and evaluation results that guided our design choices. lastly, sec. 5 summarizes our findings and outlines directions for future work. ## background a survey on monolingual, multilingual, and crosslingual research @xcite outlines the key components of an automated fact-checking pipeline: claim detection, claim prioritization, retrieval of evidence, veracity prediction, and explanation generation @xcite . in addition to these core steps, there is an additional component responsible for retrieving previously fact-checked claims. this component identifies claims that have already been verified, linking them to existing fact-checks. aligning similar claims across languages can improve fact-checking efficiency and combat misinformation more effectively. this task is commonly referred to as verified claim retrieval @xcite or claim matching @xcite , though it is also known as pfcr @xcite or fact-checked claim detection @xcite . in this work, we focus on this retrieval component. one could argue that the limitation of this task is the assumption of the existence of a fact-checked article for a given claim. however, it is important to remember that pfcr is an additional component that aims to create a shortcut in the fact-checking pipeline in case the same, perhaps reformulated, claim reappears online after it has been verified, reducing redundancy and improving response time in tackling misinformation. multiclaim dataset @xcite includes 205,751 fact-checks in 39 languages and 28,092 social media posts in 27 languages. the dataset contains 31,305 verified post-fact-check pairs, with 4,212 being crosslingual. more details on the used dataset are given in appendix a. ## system overview this section presents the system architecture shown in figure 1 , outlining the key components of the pipeline: (1) data preprocessing module, (2) retrieval-ensemble module, (3) reranking-ensemble module, (4) evaluation module. given a collection of fact-checks, our system retrieves the top k relevant fact-checks for any given claim. ## conclusion in this work, we show that reranking provides only marginal improvements over hybrid ensembling, while ensembling offers a balance between accuracy and efficiency. by combining strategic ensemble design and zero-shot retrieval, the retrievalensemble provides a scalable and effective solution for multilingual fact-checking. its simplicity leaves room for further enhancements, such as k-shot retrieval or fine-tuning with fact-check data. future work could explore improving retrieval robustness for ambiguous claims, alternative architectures, and adapting retrieval strategies based on language or claim complexity.