U ni B uc- AE at S em E val-2025 Task 7: Training Text Embedding Models for Multilingual and Crosslingual Fact-Checked Claim Retrieval (2025)

Abstract: This paper describes our approach to the SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval on both the monolingual and crosslingual tracks. Our training methodology for text embedding models combines contrastive pre-training and hard negatives mining in order to fine-tune models from the E5 family. Additionally, we introduce a novel approach for merging the results from multiple models by finding the best majority vote weighted configuration for each subtask using the validation dataset. Our team ranked 6th in the monolingual track scoring a 0.934 S@10 averaged over all languages and achieved a 0.79 S@10 on the crosslingual task, ranking 8th in this track.

## introduction fact-checking is becoming a crucial task in today's society. viral posts on social networks can reach an astonishing number of people in very little time, and false information tends to spread faster than true data @xcite . thus, automated fact-checking systems can be a useful tool in combating this problem. the previous research done in this space was mostly focused on the english language and on the monolingual task, where the fact-check and post are in the same language, bot not on the crosslingual case. the multiclaim dataset @xcite is the biggest dataset of fact-checks released to date and introduces a special section for crosslingual evaluation. this year's semeval-2025 task 7 @xcite further enhances this dataset with modifications and augmentations for this task. this paper proposes an automated fact-checking system based on text embedding models that allow the retrieval of relevant fact-checked claims through data vectorization. the implementation is based on the multilingual-e5-large-instruct @xcite and e5-large-v2 @xcite models. our approach foot_0 creates models consistent for both the monolingual and crosslingual tasks, ranking 6 th in the monolingual track and 8 th in the crosslingual track . the full results of our approach are available in the results section in ## experimental setup the social media posts are converted to a single string by concatenating the text and all the ocr lines, generating one string in the original language and one in english using the translated text. a similar process is applied to the fact-checks by concatenating the claim and title. as evaluation metrics we use the success@10 which measures whether at least one relevant item is present in the top 10 recommendations. the instruction prompt used for the multilingual-e5-large-instruct model is : "instruct: given a social media post, retrieve relevant fact-checked claims for the post". all the experiments foot_2 were conducted using adamw with a learning rate of 10 -5 with a linear scheduler of 100 warmup steps and a weight decay of 10 -5 . due to hardware limitations, the contrastive pretraining is done using a batch size of 8 pairs of matching post fact-check pairs and the hard negatives fine-tuning is done using a batch size of 4 matching pairs and 3 hard negatives for each post. we note the configurations used in the experiments in the following way: from we discovered that adding the eng and orig models to the majority vote helped to improve our result even if they had worse accuracies than eng-hard and orighard. as it can be seen from the interesting part when looking at the algorithm's results is that the configuration is not always directly proportional to the models' accuracies. for example, in the msa task the enghard model is a clear winner, but it is weighted less that the orighard model by the algorithm. for the new languages added in the test dataset we have used the average of the monolingual configurations : [0.075, 0.125, 0.4375, 0.3625]. ## conclusion our training methodology accurately creates text embedding models with strong retrieval capabilities for fact-checking multilingual tasks for both monolingual and crosslingual scenarios. finetuning using hard negatives greatly improves the accuracy of the models, while the algorithm for finding optimal weighted configurations for majority vote consistently outperforms naive approaches. our future work will revolve around testing the same training methodology on state of the art english text embedding models such as nv-embed-v2 @xcite or e5-mistral-7b-instruct @xcite . in addition, we can also use such models in knowledge distillation processes in order to improve the accuracies of the smaller models used in this paper.