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.

The main goal of this year’s SemEval Task 4 isdetecting the presence of persuasion techniquesin various meme formats. While Subtask 1targets text-only posts, Subtask 2, subsectionsa and b tackle posts containing both imagesand captions. The first 2 subtasks consist ofmulti-class and multi-label classifications, inthe context of a hierarchical taxonomy of 22different persuasion techniques.This paper proposes a solution for persuasiondetection in both these scenarios and for vari-ous languages of the caption text. Our team’smain approach consists of a Multimodal Learn-ing Neural Network architecture, having Tex-tual and Vision Transformers as its backbone.The models that we have experimented with in-clude EfficientNet and ViT as visual encodersand BERT and GPT2 as textual encoders.