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Register a new userRevisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes
إعادة النظر في تصنيف علاقات قليلة: بيانات التقييم وإخطط التصنيف
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نستكشف عدد قليل من التعلم (FSL) لتصنيف العلاقة (RC).مع التركيز على السيناريو الواقعي من FSL، والتي قد لا تنتمي مثيل الاختبار إلى أي من الفئات المستهدفة (لا شيء أعلاه، [nota])، فإننا أولا إعادة النظر في هيكل مجموعة البيانات الشعبية الأخيرة ل FSL، مشيرا إليهاتوزيع البيانات غير واقعية.لعلاج هذا، نقترح منهجية جديدة لاستكشاف بيانات اختبار القليل من الطوائم بشكل أكثر واقعية من مجموعات البيانات المتوفرة ل RC الإشراف، وتطبيقها على مجموعة البيانات المشبوكة.هذا ينتج معيارا صعبا جديدا ل FSL-RC، في أي حالة من النماذج الفنية تظهر أداء ضعيف.بعد ذلك، نقوم بتحليل مخططات التصنيف ضمن النهج الأقرب القائم على الإرشاد القائم على التضمين FSL، فيما يتعلق بالقيود التي يفرضونها على مساحة التضمين.الناجمة عن هذا التحليل، نقترح مخطط تصنيف جديد يتمثل فيه فئة NOTA كأداة مستفادة، مبين تجريبي ليكون خيارا جذابا ل FSL.
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 dataset structure for FSL, pointing out its unrealistic data distribution. To remedy this, we propose a novel methodology for deriving more realistic few-shot test data from available datasets for supervised RC, and apply it to the TACRED dataset. This yields a new challenging benchmark for FSL-RC, on which state of the art models show poor performance. Next, we analyze classification schemes within the popular embedding-based nearest-neighbor approach for FSL, with respect to constraints they impose on the embedding space. Triggered by this analysis, we propose a novel classification scheme in which the NOTA category is represented as learned vectors, shown empirically to be an appealing option for FSL.
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