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Register a new userDirectProbe: Studying Representations without Classifiers
DirectProbe: دراسة تمثيلات بدون مصنفات
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إن فهم كيفية ترميز الهيكل اللغوي في التضمين السياق يمكن أن يساعد في تفسير أدائه المثير للإعجاب عبر NLP.عادة ما تدعو النهج الحالية لتحقيقها عادة إلى تدريب الطبقات وتستخدم الدقة والمعلومات المتبادلة أو التعقيد كوكيل لخير التمثيل.في هذا العمل، نجادل بأن القيام بذلك يمكن أن يكون غير موثوق به لأن تمثيلات مختلفة قد تحتاج إلى طبقات مختلفة.نقوم بتطوير إرشادي، DirectProbe، يدرس مباشرة هندسة التمثيل من خلال البناء عند فكرة مساحة الإصدار لمهمة.تبين التجارب التي لديها العديد من المهام اللغوية والموظفة السياقية أنه، حتى بدون منصوص قياسات التدريب، يمكن أن يضيء DirectProbe الأنوار حول كيفية تمثيل مساحة التضمين ملصقات وتوقع أيضا أداء المصنف للتمثيل أيضا.
Understanding how linguistic structure is encoded in contextualized embedding could help explain their impressive performance across NLP. Existing approaches for probing them usually call for training classifiers and use the accuracy, mutual information, or complexity as a proxy for the representation's goodness. In this work, we argue that doing so can be unreliable because different representations may need different classifiers. We develop a heuristic, DirectProbe, that directly studies the geometry of a representation by building upon the notion of a version space for a task. Experiments with several linguistic tasks and contextualized embeddings show that, even without training classifiers, DirectProbe can shine lights on how an embedding space represents labels and also anticipate the classifier performance for the representation.
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