@inproceedings{gehring-etal-2017-convolutional,
    title = "A Convolutional Encoder Model for Neural Machine Translation",
    author = "Gehring, Jonas  and
      Auli, Michael  and
      Grangier, David  and
      Dauphin, Yann",
    editor = "Barzilay, Regina  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/P17-1012/",
    doi = "10.18653/v1/P17-1012",
    pages = "123--135",
    abstract = "The prevalent approach to neural machine translation relies on bi-directional LSTMs to encode the source sentence. We present a faster and simpler architecture based on a succession of convolutional layers. This allows to encode the source sentence simultaneously compared to recurrent networks for which computation is constrained by temporal dependencies. On WMT`16 English-Romanian translation we achieve competitive accuracy to the state-of-the-art and on WMT`15 English-German we outperform several recently published results. Our models obtain almost the same accuracy as a very deep LSTM setup on WMT`14 English-French translation. We speed up CPU decoding by more than two times at the same or higher accuracy as a strong bi-directional LSTM."
}