Improving Estonian Text Simplification through Pretrained Language Models and Custom Datasets
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908782849687552 |
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| author | Barbu, Eduard Muru, Meeri-Ly Malva, Sten Marcus |
| author_facet | Barbu, Eduard Muru, Meeri-Ly Malva, Sten Marcus |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_15624 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving Estonian Text Simplification through Pretrained Language Models and Custom Datasets Barbu, Eduard Muru, Meeri-Ly Malva, Sten Marcus Computation and Language 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. |
| title | Improving Estonian Text Simplification through Pretrained Language Models and Custom Datasets |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2501.15624 |