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Register a new userConstructing contrastive samples via summarization for text classification with limited annotations
بناء عينات صغيرة من خلال تلخيص لتصنيف النص مع التعليقات التوضيحية المحدودة
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لقد ظهر التعلم الناقض كطريقة لتعلم التمثيل القوي ويسهل العديد من المهام المصب المختلفة خاصة عندما تكون البيانات الخاضعة للإشراف محدودة. كيفية بناء عينات مضاءة فعالة من خلال تكبير البيانات هي مفتاح نجاحها. على عكس مهام الرؤية، لم يتم التحقيق في طريقة تكبير البيانات للتعلم المتعاقل بما فيه الكفاية في مهام اللغة. في هذه الورقة، نقترح نهج رواية لبناء عينات صغيرة لمهام اللغة باستخدام تلخيص النص. نحن نستخدم هذه العينات للتعلم المتعاقل الخاضع للإشراف للحصول على تمثيلات نصية أفضل التي تنفصل إلى حد كبير مهام تصنيف النص بشراحي محدود. لمزيد من تحسين الطريقة، نخلط عينات من فئات مختلفة وإضافة تنظيم إضافي، يدعى Mixsum، بالإضافة إلى فقدان الانتروبيا. توضح التجارب في مجموعات بيانات تصنيف النص العالمي الحقيقي (Amazon-5، YELP-5، AG News، IMDB) فعالية إطار التعلم المقاوم للضبط المقترح مع تكبير البيانات القائمة على التلخصات وانتظام Mixsum.
Contrastive Learning has emerged as a powerful representation learning method and facilitates various downstream tasks especially when supervised data is limited. How to construct efficient contrastive samples through data augmentation is key to its success. Unlike vision tasks, the data augmentation method for contrastive learning has not been investigated sufficiently in language tasks. In this paper, we propose a novel approach to construct contrastive samples for language tasks using text summarization. We use these samples for supervised contrastive learning to gain better text representations which greatly benefit text classification tasks with limited annotations. To further improve the method, we mix up samples from different classes and add an extra regularization, named Mixsum, in addition to the cross-entropy-loss. Experiments on real-world text classification datasets (Amazon-5, Yelp-5, AG News, and IMDb) demonstrate the effectiveness of the proposed contrastive learning framework with summarization-based data augmentation and Mixsum regularization.
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