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Register a new userMeta-learning for Classifying Previously Unseen Data Source into Previously Unseen Emotional Categories
التعلم التعريف لتصنيف مصدر البيانات غير المرئي سابقا في الفئة العاطفية غير المرئية سابقا
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في هذه الورقة، نضع أنفسنا في سيناريو التصنيف الذي لا يمكن الوصول إليه الطبقات المستهدفة ونوع البيانات أثناء التدريب.نستخدم نهج التعلم التلوي لتحديد ما إذا كانت المعلومات التي تم تدريبها على التلوي من بيانات الشبكة الاجتماعية المشتركة مع ملصقات عاطفة من الرعازة يمكن أن تحقق أداء تنافسي على الرسائل المسمى بفئات العاطفة المختلفة.نستفيد القليل من اللقطة التعلم لتتناسب مع سيناريو التصنيف والنظر في التعلم التعلم القائم على التعلم المتري عن طريق إعداد الشبكات النموذجية النموذجية مع تشفير محول، مدربة في أزياء بيئية.يثبت هذا النهج فعالا لالتقاط المعلومات المعوضة من مجموعة من الوسم العاطفي المصدر للتنبؤ علامات عاطفية غير مرئية مسبقا.على الرغم من أن تحويل نوع البيانات يؤدي إلى انخفاض الأداء المتوقع، فإن نهجنا التعلم التلوي يحقق نتائج لائقة عند مقارنته بالآخر تحت الإشراف بالكامل.
In this paper, we place ourselves in a classification scenario in which the target classes and data type are not accessible during training. We use a meta-learning approach to determine whether or not meta-trained information from common social network data with fine-grained emotion labels can achieve competitive performance on messages labeled with different emotion categories. We leverage few-shot learning to match with the classification scenario and consider metric learning based meta-learning by setting up Prototypical Networks with a Transformer encoder, trained in an episodic fashion. This approach proves to be effective for capturing meta-information from a source emotional tag set to predict previously unseen emotional tags. Even though shifting the data type triggers an expected performance drop, our meta-learning approach achieves decent results when compared to the fully supervised one.
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