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Register a new userZero-shot Sequence Labeling for Transformer-based Sentence Classifiers
صفر تسلسل تسلسل لتصنيف منصوص السلبية القائمة على المحولات
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نحن نبحث كيف يمكن تعديل محولات مستوى الجملة في وضع علامات تسلسل فعالة على مستوى الرمز المميز دون أي إشراف مباشر.لا تؤدي الأساليب الموجودة إلى وضع العلامات على التسلسل الصفرية جيدا عند تطبيقها على الهندسة القائمة على المحولات.نظرا لأن المحولات تحتوي على طبقات متعددة من اهتمام ذاتي متعدد الأطراف، فإن المعلومات الواردة في الجملة التي يتم توزيعها بين العديد من الرموز، مما يؤثر سلبا على أداء مستوى الرمز المميز من الصفر.نجد أن وحدة انتباه ناعمة تشجع صراحة على حدة الأوزان الاهتمام يمكن أن تتفوق بشكل كبير على الأساليب الحالية.
We investigate how sentence-level transformers can be modified into effective sequence labelers at the token level without any direct supervision. Existing approaches to zero-shot sequence labeling do not perform well when applied on transformer-based architectures. As transformers contain multiple layers of multi-head self-attention, information in the sentence gets distributed between many tokens, negatively affecting zero-shot token-level performance. We find that a soft attention module which explicitly encourages sharpness of attention weights can significantly outperform existing methods.
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