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Register a new userLearning Prototype Representations Across Few-Shot Tasks for Event Detection
التعلم النماذج الأولية عبر مهام قليلة لقطة للكشف عن الحدث
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نحن نتطلع إلى تحيز أخذ العينات والقضايا الخارجية في عدد قليل من التعلم عن اكتشاف الحدث، وهو متعقب فرعي لاستخراج المعلومات.نقترح نموذج العلاقات بين المهام التدريبية في التعلم القليل من الرصاص البارز من خلال إدخال نماذج النماذج عبر المهام.ونحن نقترح كذلك فرض اتساق التنبؤ بين المصنفين عبر المهام لجعل النموذج أكثر قوة على القيم المتطرفة.تظهر تجربتنا الواسعة تحسنا ثابتا على ثلاث مجموعات من مجموعات بيانات التعلم قليلة.تشير النتائج إلى أن نموذجنا هو أكثر قوة عند وصف بيانات المسمى لأنواع الأحداث الرواية محدودة.يتوفر شفرة المصدر على http://github.com/laiviet/fsl-proact.
We address the sampling bias and outlier issues in few-shot learning for event detection, a subtask of information extraction. We propose to model the relations between training tasks in episodic few-shot learning by introducing cross-task prototypes. We further propose to enforce prediction consistency among classifiers across tasks to make the model more robust to outliers. Our extensive experiment shows a consistent improvement on three few-shot learning datasets. The findings suggest that our model is more robust when labeled data of novel event types is limited. The source code is available at http://github.com/laiviet/fsl-proact.
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