@inproceedings{tu-etal-2020-engine,
    title = "{ENGINE}: Energy-Based Inference Networks for Non-Autoregressive Machine Translation",
    author = "Tu, Lifu  and
      Pang, Richard Yuanzhe  and
      Wiseman, Sam  and
      Gimpel, Kevin",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.251",
    pages = "2819--2826",
    abstract = "We propose to train a non-autoregressive machine translation model to minimize the energy defined by a pretrained autoregressive model. In particular, we view our non-autoregressive translation system as an inference network (Tu and Gimpel, 2018) trained to minimize the autoregressive teacher energy. This contrasts with the popular approach of training a non-autoregressive model on a distilled corpus consisting of the beam-searched outputs of such a teacher model. Our approach, which we call ENGINE (ENerGy-based Inference NEtworks), achieves state-of-the-art non-autoregressive results on the IWSLT 2014 DE-EN and WMT 2016 RO-EN datasets, approaching the performance of autoregressive models.",
}


@article{tu2020engine,
  title={ENGINE: Energy-Based Inference Networks for Non-Autoregressive Machine Translation},
  author={Tu, Lifu and Pang, Richard Yuanzhe and Wiseman, Sam and Gimpel, Kevin},
  journal={arXiv preprint arXiv:2005.00850},
  year={2020}
}