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Register a new userBiomedical Data-to-Text Generation via Fine-Tuning Transformers
توليد بيانات البيانات الطبية الحيوية عبر محولات ضبط الدقيقة
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إن جيل البيانات إلى النص (D2T) في المجال الطبي الطبيعي هو واعد - ولكن في الغالب غير مستكشفة - مجال البحث.هنا، نطبق النماذج العصبية لتوليد D2T إلى مجموعة بيانات حقيقية تتكون من منشورات الحزمة من الأدوية الأوروبية.نظهر أن المحولات التي تم ضبطها بشكل جيد قادرون على توليد نص واقعي متعدد الجملة من البيانات في المجال الطبي الطبيعي، ولكن لها قيود مهمة.ونحن نطلق أيضا عن مجموعة بيانات جديدة (bioileaflets) لنماذج جيل D2T القياسية في المجال الطبي الطبيعي.
Data-to-text (D2T) generation in the biomedical domain is a promising - yet mostly unexplored - field of research. Here, we apply neural models for D2T generation to a real-world dataset consisting of package leaflets of European medicines. We show that fine-tuned transformers are able to generate realistic, multi-sentence text from data in the biomedical domain, yet have important limitations. We also release a new dataset (BioLeaflets) for benchmarking D2T generation models in the biomedical domain.
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