@inproceedings{zeyer-etal-2018-returnn,
    title = "{RETURNN} as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition",
    author = "Zeyer, Albert  and
      Alkhouli, Tamer  and
      Ney, Hermann",
    editor = "Liu, Fei  and
      Solorio, Thamar",
    booktitle = "Proceedings of {ACL} 2018, System Demonstrations",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/P18-4022/",
    doi = "10.18653/v1/P18-4022",
    pages = "128--133",
    abstract = "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 TensorFlow 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 are able to train state-of-the-art models for translation and end-to-end models for speech recognition and show results on WMT 2017 and Switchboard. 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."
}