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

We compare the fast training and decoding speed of RETURNN of attention models for translation, due to fast CUDA LSTM kernels, and a fast pure Tensor-Flow beam search decoder. We show that a layer-wise pretraining scheme for recurrent attention models gives over 1% BLEU improvement absolute and it allows to train deeper recurrent encoder networks. Promising preliminary results on max. expected BLEU training are presented. We obtain state-of-the-art models trained on the WMT 2017 GermanEnglish translation task. We also present end-to-end model results for speech recognition on the Switchboard task. The flexibility of RETURNN allows a fast research feedback loop to experiment with alternative architectures, and its generality allows to use it on a wide range of applications.