Paraphrasing is an important task in natural language processing, involving the generation of expressions that differ in form from the original text while preserving its meaning. Automatically generating versatile and comprehensible paraphrases enhances text understanding and interpretation, and also improves human-computer interaction. We developed a paraphrasing model for Slovene, leveraging pre-trained models. Due to the computational complexity of large models, we selected a smaller version of the multilingual mT5 model and the Slovene SloT5 model, both of which are based on the transformer architecture which currently prevails in the field of natural language processing. Using the OpenSubtitles2018 dataset, we obtained Slovene and English subtitles, translating the English subtitles into Slovene to create a training set with aligned Slovene paraphrases. The dataset can be used for future research and developing models for generating Slovene paraphrases. We fine-tuned the models using this dataset and evaluated their performance with ROUGE and BERTScore metrics, as well as qualitative human judgment. The SloT5 model produced better results. By analyzing the generated paraphrases, we identified key paraphrasing strategies in Slovene and the most common errors.
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