Domain-Specific Machine Translation to Translate Medicine Brochures in English to Sorani Kurdish
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916579580575744 |
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| author | Shamal, Mariam Hassani, Hossein |
| author_facet | Shamal, Mariam Hassani, Hossein |
| contents | Access to Kurdish medicine brochures is limited, depriving Kurdish-speaking communities of critical health information. To address this problem, we developed a specialized Machine Translation (MT) model to translate English medicine brochures into Sorani Kurdish using a parallel corpus of 22,940 aligned sentence pairs from 319 brochures, sourced from two pharmaceutical companies in the Kurdistan Region of Iraq (KRI). We trained a Statistical Machine Translation (SMT) model using the Moses toolkit, conducting seven experiments that resulted in BLEU scores ranging from 22.65 to 48.93. We translated three new brochures to improve the evaluation process and encountered unknown words. We addressed unknown words through post-processing with a medical dictionary, resulting in BLEU scores of 56.87, 31.05, and 40.01. Human evaluation by native Kurdish-speaking pharmacists, physicians, and medicine users showed that 50% of professionals found the translations consistent, while 83.3% rated them accurate. Among users, 66.7% considered the translations clear and felt confident using the medications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_13609 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Domain-Specific Machine Translation to Translate Medicine Brochures in English to Sorani Kurdish Shamal, Mariam Hassani, Hossein Computation and Language Access to Kurdish medicine brochures is limited, depriving Kurdish-speaking communities of critical health information. To address this problem, we developed a specialized Machine Translation (MT) model to translate English medicine brochures into Sorani Kurdish using a parallel corpus of 22,940 aligned sentence pairs from 319 brochures, sourced from two pharmaceutical companies in the Kurdistan Region of Iraq (KRI). We trained a Statistical Machine Translation (SMT) model using the Moses toolkit, conducting seven experiments that resulted in BLEU scores ranging from 22.65 to 48.93. We translated three new brochures to improve the evaluation process and encountered unknown words. We addressed unknown words through post-processing with a medical dictionary, resulting in BLEU scores of 56.87, 31.05, and 40.01. Human evaluation by native Kurdish-speaking pharmacists, physicians, and medicine users showed that 50% of professionals found the translations consistent, while 83.3% rated them accurate. Among users, 66.7% considered the translations clear and felt confident using the medications. |
| title | Domain-Specific Machine Translation to Translate Medicine Brochures in English to Sorani Kurdish |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2501.13609 |