Enhancing Slovak Natural Language Processing: Fine-Tuning Sentence BERT Models with Translated MS MARCO V2 Data Using MADLAD-400

This study explores the enhancement of Slovak Natural Language Processing (NLP) by fine-tuning Sentence BERT models using the translated MS MARCO V2 dataset, facilitated by the MADLAD-400 language model. We translated the MS MARCO V2 dataset into Slovak and utilized this translation to train Sentence BERT models, aiming to improve the quality of vector representations for Slovak sentences. Employing a triplet network approach, we derived meaningful sentence vector representations, enabling effective comparison through cosine similarity. The research yields a newly translated database and evaluates the performance of fine-tuned Slovak vector representation models, thereby advancing Slovak search capabilities.