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Register a new userMeta-Learning for Few-Shot Named Entity Recognition
التعلم التعريف لعدد قليل من طلقة التعرف على الكيان
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تم اقتراح التعلم التلوي مؤخرا لتعلم النماذج والخوارزميات التي يمكن أن تعميمها من حفنة من الأمثلة.ومع ذلك، فإن تطبيقات التنبؤ الهيكلية والمهام النصية تشكل تحديات لخوارزميات التعلم التلوي.في هذه الورقة، نحن نطبق اثنين من خوارزميات التعلم التلوي، والشبكات النموذجية والزواحف الزواحف، إلى عدد قليل من الرصاص التعرف على الكيان (NER)، بما في ذلك طريقة لإدماج نموذج اللغة قبل التدريب والحقول العشوائية الشرطية (CRF).نقترح خطة توليد المهام لتحويل مجموعات بيانات NER الكلاسيكية إلى إعداد القليل من الرصاص، لكل من التدريب والتقييم.باستخدام ثلاث مجموعات بيانات عامة، نظهر أن خوارزميات التعلم التلوي هذه تفوق خطاس بخبراء ذو صقل معقول.بالإضافة إلى ذلك، نقترح مزيجا جديدا من الشبكات النموذجية والزواحف.
Meta-learning has recently been proposed to learn models and algorithms that can generalize from a handful of examples. However, applications to structured prediction and textual tasks pose challenges for meta-learning algorithms. In this paper, we apply two meta-learning algorithms, Prototypical Networks and Reptile, to few-shot Named Entity Recognition (NER), including a method for incorporating language model pre-training and Conditional Random Fields (CRF). We propose a task generation scheme for converting classical NER datasets into the few-shot setting, for both training and evaluation. Using three public datasets, we show these meta-learning algorithms outperform a reasonable fine-tuned BERT baseline. In addition, we propose a novel combination of Prototypical Networks and Reptile.
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