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Register a new userExploring Task Difficulty for Few-Shot Relation Extraction
استكشاف صعوبة المهمة لاستخراج علاقة ذات طلقة قليلة
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يركز استخراج علاقات قليلة (FSRE) على الاعتراف بعلاقات جديدة من خلال التعلم مع مجرد حفنة من الحالات المشروح.تم اعتماد التعلم التلوي على نطاق واسع لمثل هذه المهمة، والتي تتدرب على إنشاء مهام قليلة من الرصاص بشكل عشوائي لتعلم تمثيلات بيانات عامة.على الرغم من النتائج المثيرة للإعجاب التي تحققت، لا تزال النماذج الحالية تؤدي دون التفاادم عند التعامل مع مهام FSRE الثابتة، حيث تكون العلاقات محببة ومتشابهة لبعضها البعض.نقول هذا إلى حد كبير لأن النماذج الحالية لا تميز المهام الثابتة من سهلة في عملية التعلم.في هذه الورقة، نقدم نهجا جديدا يعتمد على التعلم المتعاقل الذي يتعلم تمثيلات أفضل من خلال استغلال معلومات الملصقات العلاقة.نحن أيضا تصميم طريقة تسمح للنموذج بتعلم تكيف كيفية التركيز على المهام الثابتة.تجارب على مجموعة بيانات قياسية توضح فعالية طريقتنا.
Few-shot relation extraction (FSRE) focuses on recognizing novel relations by learning with merely a handful of annotated instances. Meta-learning has been widely adopted for such a task, which trains on randomly generated few-shot tasks to learn generic data representations. Despite impressive results achieved, existing models still perform suboptimally when handling hard FSRE tasks, where the relations are fine-grained and similar to each other. We argue this is largely because existing models do not distinguish hard tasks from easy ones in the learning process. In this paper, we introduce a novel approach based on contrastive learning that learns better representations by exploiting relation label information. We further design a method that allows the model to adaptively learn how to focus on hard tasks. Experiments on two standard datasets demonstrate the effectiveness of our method.
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We explore few-shot learning (FSL) for relation classification (RC). Focusing on the realistic scenario of FSL, in which a test instance might not belong to any of the target categories (none-of-the-above, [NOTA]), we first revisit the recent popular
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