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Register a new userIncorporating Domain Knowledge into Language Transformers for Multi-Label Classification of Chinese Medical Questions
دمج معرفة المجال إلى محولات اللغة للحصول على تصنيف متعدد التسميات للأسئلة الطبية الصينية
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في هذه الورقة، نقترح آلية تسريب المعرفة لإدماج معرف المجال إلى محولات اللغة. يعتبر البيانات الخاضعة للإشراف ضعيفا كمصدر رئيسي للاستحواذ على المعرفة. نحن ندرب نماذج اللغة مسبقا لالتقاط المعرفة الملثمين بالتركيز والجوانب ثم قم بضبطها للحصول على أداء أفضل على مهام المصب. نظرا لعدم وجود مجموعات بيانات متوفرة للجمهور لتصنيف متعدد التسميات للأسئلة الطبية الصينية، زحفنا أسئلة من منتديات السؤال الطبي / الإجابة وشرحتها يدويا باستخدام ثمانية فصول محددة مسبقا: الأشخاص والمنظمات، والأعراض، والسبب، والفحص والمرض، والمعلومات، المكون، والعلاج. أخيرا، ما مجموعه 1814 سؤالا مع 2،340 ملميا. يحتوي كل سؤال على متوسط 1.29 ملصقات. استخدمنا موسوعة بايدو الطبية كمورد المعرفة. تم تنفيذ برت محولين وروبرتا لمقارنة الأداء على مجموعات بياناتنا المبنية. أظهرت النتائج التجريبية أن نموذجنا المقترح مع آلية ضخ المعرفة يمكن أن يحقق أداء أفضل، بغض النظر عن متري التقييم بما في ذلك ماكرو F1 أو مايكرو F1 أو الدقة المزدوجة الواردة في الدقة الفرعية.
In this paper, we propose a knowledge infusion mechanism to incorporate domain knowledge into language transformers. Weakly supervised data is regarded as the main source for knowledge acquisition. We pre-train the language models to capture masked knowledge of focuses and aspects and then fine-tune them to obtain better performance on the downstream tasks. Due to the lack of publicly available datasets for multi-label classification of Chinese medical questions, we crawled questions from medical question/answer forums and manually annotated them using eight predefined classes: persons and organizations, symptom, cause, examination, disease, information, ingredient, and treatment. Finally, a total of 1,814 questions with 2,340 labels. Each question contains an average of 1.29 labels. We used Baidu Medical Encyclopedia as the knowledge resource. Two transformers BERT and RoBERTa were implemented to compare performance on our constructed datasets. Experimental results showed that our proposed model with knowledge infusion mechanism can achieve better performance, no matter which evaluation metric including Macro F1, Micro F1, Weighted F1 or Subset Accuracy were considered.
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