Mix-MoE applies separate LM and MT expert groups in two post-pretraining stages with Fourier-enhanced routing to reduce parameter interference and improve multilingual MT over baselines.
Deep Learning Based Natural Language Processing for End to End Speech Translation
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abstract
Deep Learning methods employ multiple processing layers to learn hierarchial representations of data. They have already been deployed in a humongous number of applications and have produced state-of-the-art results. Recently with the growth in processing power of computers to be able to do high dimensional tensor calculations, Natural Language Processing (NLP) applications have been given a significant boost in terms of efficiency as well as accuracy. In this paper, we will take a look at various signal processing techniques and then application of them to produce a speech-to-text system using Deep Recurrent Neural Networks.
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Mix-MoE: Improving Multilingual Machine Translation of Large Language Models through Mixed MoEs
Mix-MoE applies separate LM and MT expert groups in two post-pretraining stages with Fourier-enhanced routing to reduce parameter interference and improve multilingual MT over baselines.