Shouth NLP at S em E val-2025 Task 7: Multilingual Fact-Checking Retrieval Using Contrastive Learning (2025)

Abstract: We present a multilingual fact-checking re-trieval system for the SemEval-2025 task ofmatching social media posts with relevant factchecks. Our approach utilizes a contrastivelearning framework built on the multilingual E5model architecture, fine-tuned on the provideddataset. The system achieves a Success@10score of 0.867 on the official test set, with per-formance variations between languages. Wedemonstrate that input prefixes and language-specific corpus filtering significantly improveretrieval performance. Our analysis reveals in-teresting patterns in cross-lingual transfer, withspecifically strong results on Malaysian andThai languages. We make our code public forfurther research and development.

## introduction the proliferation of misinformation on social media is referred by social scientists as a threat to the integrity of democracy and the public sphere @xcite this scenario has increasingly created an urgent need for automated fact-checking systems that can retrieve relevant fact-checks for dubious claims. the semeval-2025 task @xcite addresses this challenge by requiring participants to develop systems that can retrieve the most relevant fact checks for social media posts from a large corpus of 272,447 fact checks among multiple languages. in this paper, we present our approach to this task, which utilizes a fine-tuned multilingual embedding model based on the e5 architecture @xcite . our system employs a contrastive learning framework to optimize the semantic matching between posts and fact checks on fine-tuning. we focus in particular on multilingual representation learning and cross-lingual retrieval functions. the code for our system is publicly available on github foot_0 . our main contributions include: (1) an analysis of various model architectures and their performance among different languages, (2) an examination of how input prefixes affect retrieval accuracy, and (3) a demonstration of the importance of language-specific corpus filtering for improving retrieval performance. our system achieved an average success@10 score of 0.867 on the official test set, ranking 20th out of 28 published participants. ## background automated fact-checking has emerged as a critical tool in combating the spread of misinformation online. @xcite ) speak about the immense volume of in-formation shared online, and suggest and evaluate different methods to detect fake news. the semeval-2025 task @xcite focuses especially on multilingual fact-check retrieval, where the goal is to match social media posts with relevant fact-checks from a large multilingual corpus. most automated fact-checking system consist of three steps @xcite : first, the claim de-tection (is this check-worthy?); then, the evidence retrieval (given a claim, retrieve relevant informa-tion to verify it); and lastly and claim verification (given a claim and evidence, define if the claim is true or false). the task @xcite has two subtasks: monolingual retrieval and cross-lingual retrieval. we focus only on the monolingual subtask. recent advances in multilingual embedding models have shown promising results for multilingual retrieval tasks @xcite . these models learn a shared semantic space between languages, allowing for effective comparison of text regardless of the source language. in particular, contrastive learning approaches have proven effective for training such models @xcite , as they explicitly op-timize for similarity between related texts while pushing unrelated texts apart in the embedding space. in this paper, we present our retrieval approach to this task, which uses a fine-tuned multilingual embedding model based on the e5 architecture @xcite . this model has demonstrated strong performance on multilingual retrieval benchmarks @xcite by combining transformer-based encoders with contrastive pre-training. we additionally fine-tune this model on the task-specific data to improve its performance for fact-check retrieval. ## system overview our system addresses the challenge of retrieving relevant fact checks from a large corpus of 272,447 documents by implementing a fine-tuned contrastive learning approach based on the multilingual e5 model architecture. the system consists of three main components: (1) a text embedding model, (2) a contrastive learning framework, and (3) a retrieval pipeline. the core of our system is built upon the multilingual e5 model @xcite , which we improve with a custom architecture. the model architecture is illustrated in figure 1 , consisting of a transformer encoder, mean pooling layer, dense projection layer with gelu activation, and l2 normalization for cosine similarity calculation. the model processes both posts and fact checks using language-specific prefixes (query: and passage: respectively) to optimize the semantic matching between them. we trained our model using the multiple negatives ranking loss (mnrl) @xcite , which can be formulated as: figure 1 : architecture of the embedding model used in our system. the model processes both posts and fact checks using language-specific prefixes to optimize semantic matching. the retrieval process follows these steps: this pipeline is set for both monolingual and cross-lingual retrieval, with special consideration for language-specific distinctions in the embedding space. ## results we evaluate our system's performance on both the validation and test sets, analyzing the impact of different model configurations and language-specific performance. our system achieved an average success@10 score of 0.867 on the official test set, ranking 20th out of 28 published participants. the results demonstrate considerable variability in model performance across languages, with malaysian (msa) showing the strongest performance at 0.978 and english (eng) and portuguese (por) showing the lowest at 0.796. this disparity suggests that linguistic factors may play a significant role in retrieval performance.