@inproceedings{escolano-etal-2019-bilingual,
    title = "From Bilingual to Multilingual Neural Machine Translation by Incremental Training",
    author = "Escolano, Carlos  and
      Costa-juss{\`a}, Marta R.  and
      Fonollosa, Jos{\'e} A. R.",
    editor = "Alva-Manchego, Fernando  and
      Choi, Eunsol  and
      Khashabi, Daniel",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/P19-2033/",
    doi = "10.18653/v1/P19-2033",
    pages = "236--242",
    abstract = "Multilingual Neural Machine Translation approaches are based on the use of task specific models and the addition of one more language can only be done by retraining the whole system. In this work, we propose a new training schedule that allows the system to scale to more languages without modification of the previous components based on joint training and language-independent encoder/decoder modules allowing for zero-shot translation. This work in progress shows close results to state-of-the-art in the WMT task."
}