@inproceedings{wang-etal-2021-cross,
    title = "Cross-lingual Supervision Improves Unsupervised Neural Machine Translation",
    author = "Wang, Mingxuan  and
      Bai, Hongxiao  and
      Zhao, Hai  and
      Li, Lei",
    editor = "Kim, Young-bum  and
      Li, Yunyao  and
      Rambow, Owen",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.naacl-industry.12/",
    doi = "10.18653/v1/2021.naacl-industry.12",
    pages = "89--96",
    abstract = "We propose to improve unsupervised neural machine translation with cross-lingual supervision (), which utilizes supervision signals from high resource language pairs to improve the translation of zero-source languages. Specifically, for training En-Ro system without parallel corpus, we can leverage the corpus from En-Fr and En-De to collectively train the translation from one language into many languages under one model. {\%} is based on multilingual models which require no changes to the standard unsupervised NMT. Simple and effective, significantly improves the translation quality with a big margin in the benchmark unsupervised translation tasks, and even achieves comparable performance to supervised NMT. In particular, on WMT{'}14 -tasks achieves 37.6 and 35.18 BLEU score, which is very close to the large scale supervised setting and on WMT{'}16 -tasks achieves 35.09 BLEU score which is even better than the supervised Transformer baseline."
}