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We compare the fast training and decoding speed of RETURNN of attentionmodels for translation, due to fast CUDA LSTM kernels, and a fast pureTensorFlow beam search decoder. We show that a layer-wise pretraining schemefor recurrent attention models gives over 1% BLEU improvement absolute and itallows to train deeper recurrent encoder networks. Promising preliminaryresults on max. expected BLEU training are presented. We are able to trainstate-of-the-art models for translation and end-to-end models for speechrecognition and show results on WMT 2017 and Switchboard. The flexibility ofRETURNN allows a fast research feedback loop to experiment with alternativearchitectures, and its generality allows to use it on a wide range ofapplications.

RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition