@inproceedings{ha-etal-2016-toward,
    title = "Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder",
    author = "Ha, Thanh-Le  and
      Niehues, Jan  and
      Waibel, Alex",
    editor = {Cettolo, Mauro  and
      Niehues, Jan  and
      St{\"u}ker, Sebastian  and
      Bentivogli, Luisa  and
      Cattoni, Rolando  and
      Federico, Marcello},
    booktitle = "Proceedings of the 13th International Conference on Spoken Language Translation",
    month = dec # " 8-9",
    year = "2016",
    address = "Seattle, Washington D.C",
    publisher = "International Workshop on Spoken Language Translation",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2016.iwslt-1.6/",
    abstract = "In this paper, we present our first attempts in building a multilingual Neural Machine Translation framework under a unified approach in which the information shared among languages can be helpful in the translation of individual language pairs. We are then able to employ attention-based Neural Machine Translation for many-to-many multilingual translation tasks. Our approach does not require any special treatment on the network architecture and it allows us to learn minimal number of free parameters in a standard way of training. Our approach has shown its effectiveness in an under-resourced translation scenario with considerable improvements up to 2.6 BLEU points. In addition, we point out a novel way to make use of monolingual data with Neural Machine Translation using the same approach with a 3.15-BLEU-score gain in IWSLT`16 English{\textrightarrow}German translation task."
}