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مقياس الصناعة التعليم شبه الإشراف على فهم اللغة الطبيعية
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تقدم هذه الورقة خط أنابيب التعلم شبه الإشرافه (SSL) على أساس إطار المعلم الطالب، الذي يزداد ملايين الأمثلة غير المستمرة لتحسين مهام فهم اللغة الطبيعية (NLU). نحن نبحث في سؤالين يتعلق باستخدام البيانات غير المسبقة في سياق الإنتاج SSL: 1) كيفية تحديد عينات من تجمع بيانات ضخمة غير مسفوقة مفيدة لتدريب SSL، و 2) كيف تؤثر البيانات المحددة على أداء حالة مختلفة من بين تقنيات SSL-Art. نقارن أربعة تقنيات SSL المستخدمة على نطاق واسع، والتسمية الزائفة (PL)، وقطاع المعرفة (KD)، والتدريب الخصم الافتراضي (VAT) والتدريب عبر الرؤية (CVT) جنبا إلى جنب مع طريقتين اختيار البيانات بما في ذلك الاختيار القائم على اللجنة وتحسين الأسفل اختيار مقرها. نحن ندرس مزيدا من فوائد وعيوب هذه التقنيات عند تطبيقها على تصنيف تكاليف النية (IC) ومهام التعرف على الكيان المسماة (NER)، وتوفير المبادئ التوجيهية التي تحدد عندما تكون كل من هذه الطرق مفيدة لتحسين أنظمة NLU كبيرة الحجم.
This paper presents a production Semi-Supervised Learning (SSL) pipeline based on the student-teacher framework, which leverages millions of unlabeled examples to improve Natural Language Understanding (NLU) tasks. We investigate two questions related to the use of unlabeled data in production SSL context: 1) how to select samples from a huge unlabeled data pool that are beneficial for SSL training, and 2) how does the selected data affect the performance of different state-of-the-art SSL techniques. We compare four widely used SSL techniques, Pseudo-label (PL), Knowledge Distillation (KD), Virtual Adversarial Training (VAT) and Cross-View Training (CVT) in conjunction with two data selection methods including committee-based selection and submodular optimization based selection. We further examine the benefits and drawbacks of these techniques when applied to intent classification (IC) and named entity recognition (NER) tasks, and provide guidelines specifying when each of these methods might be beneficial to improve large scale NLU systems.
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