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Register a new userHow to Select One Among All ? An Empirical Study Towards the Robustness of Knowledge Distillation in Natural Language Understanding
كيفية اختيار واحد بين الكل؟دراسة تجريبية نحو تقلير تقارير المعرفة في فهم اللغة الطبيعية
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تقطير المعرفة (KD) هي خوارزمية ضغط نموذجية تساعد في نقل المعرفة في شبكة عصبية كبيرة في واحدة أصغر.على الرغم من أن KD قد أظهرت وعد على مجموعة واسعة من تطبيقات معالجة اللغة الطبيعية (NLP)، يفهم القليل حول كيفية مقارنة خوارزمية KD واحدة مع آخر وما إذا كانت هذه الأساليب يمكن أن تكون مجانية لبعضها البعض.في هذا العمل، نقوم بتقييم خوارزميات KD المختلفة على اختبار داخل المجال والخروج والمصدري.نقترح إطارا لتقييم متانة الخصومة لخوارزميات متعددة الدكتوراط.علاوة على ذلك، نقدم خوارزمية KD جديدة، مجتمعة د.ك، والتي تستفيد من نهجين واعدين (مخطط تدريب أفضل وزعم البيانات أكثر كفاءة).تظهر النتائج التجريبية الواسعة لدينا أن مجتمعة - KD تحقق نتائج أحدث النتائج على مرجع الغراء، وتعميم خارج المجال، ومتانة الخصومة مقارنة بالأساليب التنافسية.
Knowledge Distillation (KD) is a model compression algorithm that helps transfer the knowledge in a large neural network into a smaller one. Even though KD has shown promise on a wide range of Natural Language Processing (NLP) applications, little is understood about how one KD algorithm compares to another and whether these approaches can be complimentary to each other. In this work, we evaluate various KD algorithms on in-domain, out-of-domain and adversarial testing. We propose a framework to assess adversarial robustness of multiple KD algorithms. Moreover, we introduce a new KD algorithm, Combined-KD, which takes advantage of two promising approaches (better training scheme and more efficient data augmentation). Our extensive experimental results show that Combined-KD achieves state-of-the-art results on the GLUE benchmark, out-of-domain generalization, and adversarial robustness compared to competitive methods.
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