
Innovating Neural Machine Translation with Arul Menezes - #458
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
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Exploring Model Compression Techniques in Neural Machine Translation
This chapter explores the processes of model compression in neural machine translation, focusing on techniques like knowledge distillation and quantization. It addresses the challenges faced, such as overfitting and neural hallucination, while highlighting the need for diverse training data to improve the performance of smaller models.
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