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Register a new userNoisy Self-Knowledge Distillation for Text Summarization
تقطير المعرفة الذاتية صاخبة لتلخيص النص
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في هذه الورقة، نطبق تقطير المعرفة الذاتية لتلخيص النص الذي نقوله أنه يمكن أن يخفف من مشاكل في الحد الأقصى للتدريب احتمالية على مجموعات بيانات مرجعية واحدة وصاخبة.بدلا من الاعتماد على ملصقات توضيحية ذات ساخنة واحدة، يتم تدريب نموذج تلخيص الطلاب لدينا مع توجيهات من المعلم الذي يولد ملصقات سلاسة للمساعدة في تنظيم التدريب.علاوة على ذلك، لتحسين نموذج عدم اليقين أثناء التدريب، نقدم إشارات متعددة الضوضاء لكل من نماذج المعلم والطلاب.نوضح تجريبيا في ثلاثة معايير أن إطار عملائنا يعزز أداء كل من الملاحظات المحددة أو غير مسبوقة تحقيق نتائج حالة من الفنون.
In this paper we apply self-knowledge distillation to text summarization which we argue can alleviate problems with maximum-likelihood training on single reference and noisy datasets. Instead of relying on one-hot annotation labels, our student summarization model is trained with guidance from a teacher which generates smoothed labels to help regularize training. Furthermore, to better model uncertainty during training, we introduce multiple noise signals for both teacher and student models. We demonstrate experimentally on three benchmarks that our framework boosts the performance of both pretrained and non-pretrained summarizers achieving state-of-the-art results.
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Knowledge Distillation (KD) is extensively used to compress and deploy large pre-trained language models on edge devices for real-world applications. However, one neglected area of research is the impact of noisy (corrupted) labels on KD. We present,
to the best of our knowledge, the first study on KD with noisy labels in Natural Language Understanding (NLU). We document the scope of the problem and present two methods to mitigate the impact of label noise. Experiments on the GLUE benchmark show that our methods are effective even under high noise levels. Nevertheless, our results indicate that more research is necessary to cope with label noise under the KD.
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Pretrained transformer-based encoders such as BERT have been demonstrated to achieve state-of-the-art performance on numerous NLP tasks. Despite their success, BERT style encoders are large in size and have high latency during inference (especially o
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