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        <title>Improving Estonian Text Simplification through Pretrained Language Models and Custom Datasets</title>
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        <namePart type="given">Eduard</namePart>
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        <namePart type="given">Meeri-Ly</namePart>
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            <title>Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era</title>
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            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>This paper presents a method for text simplification based on two neural architectures: a neural machine translation (NMT) model and a fine-tuned large language model (LLaMA). Given the scarcity of existing resources for Estonian, a new dataset was created by combining manually translated corpora with GPT-4.0-generated simplifications. OpenNMT was selected as a representative NMT-based system, while LLaMA was fine-tuned on the constructed dataset. Evaluation shows LLaMA outperforms OpenNMT in grammaticality, readability, and meaning preservation. These results underscore the effectiveness of large language models for text simplification in low-resource language settings. The complete dataset, fine-tuning scripts, and evaluation pipeline are provided in a publicly accessible supplementary package to support reproducibility and adaptation to other languages.</abstract>
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%0 Conference Proceedings
%T Improving Estonian Text Simplification through Pretrained Language Models and Custom Datasets
%A Barbu, Eduard
%A Muru, Meeri-Ly
%A Malva, Sten Marcus
%Y Angelova, Galia
%Y Kunilovskaya, Maria
%Y Escribe, Marie
%Y Mitkov, Ruslan
%S Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F barbu-etal-2025-improving
%X This paper presents a method for text simplification based on two neural architectures: a neural machine translation (NMT) model and a fine-tuned large language model (LLaMA). Given the scarcity of existing resources for Estonian, a new dataset was created by combining manually translated corpora with GPT-4.0-generated simplifications. OpenNMT was selected as a representative NMT-based system, while LLaMA was fine-tuned on the constructed dataset. Evaluation shows LLaMA outperforms OpenNMT in grammaticality, readability, and meaning preservation. These results underscore the effectiveness of large language models for text simplification in low-resource language settings. The complete dataset, fine-tuning scripts, and evaluation pipeline are provided in a publicly accessible supplementary package to support reproducibility and adaptation to other languages.
%U https://aclanthology.org/2025.ranlp-1.16/
%P 133-142

Markdown (Informal)

[Improving Estonian Text Simplification through Pretrained Language Models and Custom Datasets](https://aclanthology.org/2025.ranlp-1.16/) (Barbu et al., RANLP 2025)

• Improving Estonian Text Simplification through Pretrained Language Models and Custom Datasets (Barbu et al., RANLP 2025)

ACL

• Eduard Barbu, Meeri-Ly Muru, and Sten Marcus Malva. 2025. Improving Estonian Text Simplification through Pretrained Language Models and Custom Datasets. In Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era, pages 133–142, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.