M ulti M ind at S em E val-2025 Task 7: Crosslingual Fact-Checked Claim Retrieval via Multi-Source Alignment (2025)

Abstract: This paper presents our system for SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval. In an era where misinformation spreads rapidly, effective fact-checking is increasingly critical. We introduce {textbf{TriAligner}}, a novel approach that leverages a dual-encoder architecture with contrastive learning and incorporates both native and English translations across different modalities. Our method effectively retrieves claims across multiple languages by learning the relative importance of different sources in alignment. To enhance robustness, we employ efficient data preprocessing and augmentation using large language models while incorporating hard negative sampling to improve representation learning. We evaluate our approach on monolingual and crosslingual benchmarks, demonstrating significant improvements in retrieval accuracy and fact-checking performance over baselines.

## introduction the rapid spread of misinformation on online platforms has become a major global challenge @xcite . false claims can now easily cross linguistic and regional boundaries, magnifying their potential impact @xcite @xcite . the multilingual nature of the internet amplifies this issue, as misinformation quickly spreads across different language communities, making it harder to track and counteract @xcite . while professional fact-checkers work tirelessly to verify misleading information, the sheer volume of online content, often published in multiple languages @xcite , makes manual verification increasingly impractical @xcite . the growing speed and scale of misinformation make these efforts more urgent, yet even the most diligent fact-checkers cannot keep pace. ## background and related work the semeval task features two tracks: monolingual and cross-lingual. in the monolingual track, the post and claim are written in the same language, whereas the cross-lingual track pairs a post with a claim in a different language. earlier datasets for previously fact-checked claim retrieval (pfcr), such as checkthat! (barrón-cedeño et al., 2020), primarily focused on english and arabic, and included manually filtered social media posts to ensure reliability. in contrast, we use the multiclaim dataset @xcite , a large and linguistically diverse resource designed to support multilingual and cross-lingual pfcr. multi-claim comprises approximately 206k fact-checks in 39 languages and over 28k social media posts in 27 languages. it also includes machine-translated posts and ocr-processed images, further facilitating experimentation across languages and modalities. regarding methodologies, bm25 and neural text embedding models (tems) are commonly used in pfcr tasks. @xcite relied on bm25 for its efficiency and robustness in monolingual retrieval. additionally, @xcite applied the integration of neural and traditional approaches to the fact-checking problem on twitter. however, multilingual and cross-lingual retrieval often requires more sophisticated embedding-based solutions. @xcite shows that embedding models, especially when enhanced with machine translation and supervised fine-tuning, can outperform traditional retrieval methods like bm25 in multilingual contexts. recent studies have explored using large language models (llms) for multilingual pfcr. @xcite evaluated seven llms across 20 languages, finding that while llms perform well for high-resource languages, they struggle with lowresource ones. translating texts into english notably improved performance for low-resource languages. similarly, @xcite assessed the multilingual fact-checking abilities of five llms across five languages using various prompting techniques. they found that zero-shot prompting with self-consistency decoding was most effective, and interestingly, llms showed better fact-checking performance in low-resource languages, suggesting potential for mitigating language disparities in pfcr tasks. additional research has extended pfcr by incorporating modalities such as visual data @xcite , abstractive summarization @xcite , and key sentence identification @xcite to improve retrieval accuracy and contextual understanding. ## system overview figure 1 provides an overview of our method. we employ various techniques to enhance model performance, which we detail in the following sections: ## experimental evaluation and results for evaluation, we perform the retrieval task based on the alignment scores between query social media posts and fact-checked claims. the system returns the top-k most relevant fact-checked claims for each social media post in both monolingual and crosslingual configurations. in the monolingual setting, the model retrieves relevant fact-checked claims from the same language as the query post, while in the crosslingual setting, the model searches across fact-checked claims in all available languages. for the architecture of our core trialigner system, the first layer consists of four encoders with similar structures, each processing different language sources for posts and facts. each encoder includes a linear layer that reduces the input dimension from 1024 to 256, followed by batch normalization, a relu activation function, dropout with a probability of 0.2, and a final linear layer that preserves the 256-dimensional representation. in the second stage, after concatenating the outputs from the previous layer's native and english translation sources, the processed embeddings are fed into additional encoders. each of these encoders contains a linear layer that reduces dimensions from 512 to 256, followed by a relu activation function and another linear layer that maintains the 256-dimensional representation. finally, the outputs of the post encoders and initial encoders are normalized, and all are used in the matrix construction phase, where the relevance scores between facts and posts are computed. due to the dataset size, we did not make the encoders too complex to avoid overfitting. computational constraints required us to construct the final similarity matrix incrementally by processing the data in batches. for more details of our experiment setup and hyperparameters, see appendix ( §d). we evaluated our model with k values of 1, 10, and 20, reporting two metrics: success@k (s@k) and recall@k (r@k). success@k equals 1 when at least one associated fact-check for a post is retrieved in the top k results, and 0 otherwise. recall@k is more stringent, measuring the proportion of relevant fact-checks retrieved in the top k. for example, if a social media post has two associated fact-checks and only one appears in the top k results, the recall@k score for that post is 0.5. the formal definitions of these metrics are as follows: ## conclusion in this work, we introduce trialigner, a contrastive learning-based approach for multilingual and crosslingual fact-checking, leveraging a dual encoder setup and hard negative samples to improve fact-checked claim retrieval. through data preprocessing and augmentation, our method improves robustness across diverse languages and social media contexts. experimental results demonstrate the effectiveness of our approach in retrieving relevant evidence and mitigating misinformation. future work can explore integrating additional modalities, refining negative sampling strategies, and adapting the model to evolving misinformation patterns. our findings highlight the value of contrastive learning for fast and accurate fact-checking in a globally connected digital landscape. a detailed discussion of limitations and further potential future directions can be found in appendix ( §c).