fact check AI at S em E val-2025 Task 7: Multilingual and Crosslingual Fact-checked Claim Retrieval (2025)

Abstract: The SemEval-2025 Task 7 on Multilingualand Crosslingual Fact-checked Claim Retrievalfocuses on retrieving relevant Fact-checkedclaims for social media Posts across multiplelanguages. This task is particularly challengingdue to linguistic barriers and the vast numberof languages Fact-checkers must consider.In this work, I approach the problem as aLearning-to-Rank task and solve it using abi-encoder-based model, fine-tuned on a pre-trained transformer optimized for sentence sim-ilarity. For the monolingual task, training wasperformed in both the source languages andtheir English translations. For cross-lingualretrieval, the training relied on English transla-tions.Most fine-tuned models have fewer than 500Mparameters, and the training was carried outefficiently using kaggle T4 GPUs with paral-lelization. Despite this lightweight setup, ourapproach achieved 92% Success@10 for mul-tilingual retrieval and 80% Success@10 forcross-lingual retrieval, securing 5th place inthe cross-lingual track and 10th place in themultilingual setting.

## introduction the rapid spread and multilingual nature of online disinformation pose a significant challenge to traditional fact-checking workflows. semeval-2025 task 7: multilingual and crosslingual fact-checked claim retrieval @xcite addresses this issue by aiming to automate the retrieval of previously verified claims across languages. the corpus for this task consists of factchecked claims and social media posts in eight languages: french, spanish, english, portuguese, thai, german, malay, and arabic. to further evaluate the generalization of the model, the evaluation step introduced two new unseen languages, pol and tur, underlining the demand for strong multilingual and cross-lingual retrieval approaches. our approach tries to tackle these challenges by improving performance both in multilingual and crosslingual scenarios. we utilize lightweight, scalable transformer models that can be trained efficiently even on modest hardware. our approach produces rich semantic representations and utilizes methods such as layer freezing and gradient checkpointing to tune the trade-off between efficiency and effectiveness. the outcome is a practical, adaptable tool for global misinformation detection that addresses both retrieval accuracy and computational scalability. we trained independent models for each language and utilized english translations of the source language inputs to boost the retrieval. for cross-lingual tasks, we predominantly used english translations to achieve consistency. in multilingual environments, we retrieved the top 10 fact verify claims from several models and ranked them according to their scores. for crosslanguage retrievals, we repeated the same process using outputs from a five-fold ensemble. this allowed us to combine diversity, accuracy, and speed very well. our approach finally ended up being 5th in crosslanguage retrieval and 10th in multilingual retrieval. all models were less than 500m parameters, and training was done using only two kaggle t4 gpus showing that high performance is attainable even with constrained computational resources. ## background the semeval-2025 task 7 is all about creating systems that can find reliable fact-checked claims for social media posts. this helps fact-checkers who deal with different languages. it's tough to look for fact-checks in many languages by hand. so, we need to automate this to save time and effort. there are two main parts to the task: multilingual retrieval: here, both the social media post and the fact-check are in the same language. crosslingual retrieval: in this case, the post and the fact-check are in different languages. this needs strong techniques for matching across languages. ## system overview our system uses a bi-encoder setup @xcite . this helps match posts and factchecks quickly. we also use smart pooling methods to improve pre-trained sentence similarity models. the model has a few important parts: ## acknowledgments we would like to thank the semeval-2025 task 7 organizers for providing the cross-lingual and multilingual fact-check retrieval corpus and evaluation setup.we thank the anonymous reviewers very much for their thoughtful and constructive comments, which helped significantly in improving the quality of our manuscript. relevant answers of your reviews can be found in the sections (training strategy) 4.2.1, (end-to-end flow) 3.6 and (risks and performance insights) 6.3 in our paper. ## conclusion we evaluated how effectively different multilingual em-embedding models perform for multi and cross-language retrieval. we established that their effectiveness differs with the language, especially in low-resource environments like pol and tur