用专业模型将英文药剂说明书翻译成索拉尼库尔德语，提升健康信息可及性。
Domain-Specific Machine Translation to Translate Medicine Brochures in English to Sorani Kurdish
- 基于2.3万句对训练统计机器翻译模型，针对药剂说明书优化。
- 引入医学词典后，翻译准确率显著提升，最高BLEU达56.87。
- 本地医生和用户评价显示译文清晰可信，适合医疗场景使用。
库尔德语药剂说明书获取受限，导致库尔德语社群难以获得关键健康信息。为解决此问题，我们开发了一种专用机器翻译（MT）模型，将英文药剂说明书翻译成索拉尼库尔德语。数据来自伊拉克库尔德斯坦地区（KRI）两家制药公司提供的319份说明书，构建了包含22,940组对齐句子的平行语料库。使用Moses工具包训练统计机器翻译（SMT）模型，共进行七次实验，取得22.65至48.93的BLEU分数。为改进评估，我们翻译了三份新说明书，发现存在未知词汇。通过后处理结合医学词典，成功提升翻译质量，对应BLEU分数分别为56.87、31.05和40.01。由母语为库尔德语的药剂师、医生和患者进行人工评估，50%专业人士认为译文一致，83.3%认为准确；66.7%使用者表示理解清晰并有信心用药。
原文摘要 · Abstract (English)
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.
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