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Register a new userUnsupervised Data Augmentation with Naive Augmentation and without Unlabeled Data
تكبير البيانات غير المزعجة مع زيادة ساذجة وبدون بيانات غير مسافة
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تعظيم البيانات غير المزعجة (UDA) هي تقنية شبه بيئية تنطبق على فقدان الاتساق لمعاقبة الاختلافات بين تنبؤات النماذج على (أ) أمثلة ملحوظة (غير مسفحة)؛ و (ب) الأمثلة الواضحة المقابلة التي تم إنتاجها عبر تكبير البيانات. في حين أن UDA اكتسبت شعبية لتصنيف النصوص، فإن الأسئلة المفتوحة باقية من قرارات التصميم ضرورية وكيفية تمديد الطريقة لتسلسل مهام وضع العلامات. في هذه الورقة، نعيد فحص UDA وإظهار فعاليتها في العديد من المهام المتسلسلة. مساهمتنا الرئيسية هي دراسة تجريبية ل UDA لتأسيس مكونات الخوارزمية التي تمنح استحقاقات NLP. وخاصة، على الرغم من أن العمل السابق قد أكد على استخدام تقنيات تكبير ذكية بما في ذلك الترجمة ذات الترجمة المرجانية، نجد أن التناسق بين التنبؤات المخصصة للكلمات الملحوظة والمستبدلة غالبا ما تسفر عن فوائد قابلة للمقارنة (أو أكبر) مقارنة بنماذج الاضطرابات الأكثر تعقيدا. علاوة على ذلك، نجد أن تطبيق فقدان اتساق UDA يوفر مكاسب ذات مغزى دون أي بيانات غير قابلة للتحقيق على الإطلاق، أي في إعداد قياسي إشرافي. باختصار، لا تحتاج UDA إلى عدم إدراكها لتحقيق الكثير من فوائدها المذكورة، ولا تتطلب تكبير بيانات معقدة لتكون فعالة.
Unsupervised Data Augmentation (UDA) is a semisupervised technique that applies a consistency loss to penalize differences between a model's predictions on (a) observed (unlabeled) examples; and (b) corresponding noised' examples produced via data augmentation. While UDA has gained popularity for text classification, open questions linger over which design decisions are necessary and how to extend the method to sequence labeling tasks. In this paper, we re-examine UDA and demonstrate its efficacy on several sequential tasks. Our main contribution is an empirical study of UDA to establish which components of the algorithm confer benefits in NLP. Notably, although prior work has emphasized the use of clever augmentation techniques including back-translation, we find that enforcing consistency between predictions assigned to observed and randomly substituted words often yields comparable (or greater) benefits compared to these more complex perturbation models. Furthermore, we find that applying UDA's consistency loss affords meaningful gains without any unlabeled data at all, i.e., in a standard supervised setting. In short, UDA need not be unsupervised to realize much of its noted benefits, and does not require complex data augmentation to be effective.
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