@inproceedings{ko-etal-2021-adapting,
    title = "Adapting High-resource {NMT} Models to Translate Low-resource Related Languages without Parallel Data",
    author = "Ko, Wei-Jen  and
      El-Kishky, Ahmed  and
      Renduchintala, Adithya  and
      Chaudhary, Vishrav  and
      Goyal, Naman  and
      Guzm{\'a}n, Francisco  and
      Fung, Pascale  and
      Koehn, Philipp  and
      Diab, Mona",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-long.66",
    doi = "10.18653/v1/2021.acl-long.66",
    pages = "802--812",
    abstract = "The scarcity of parallel data is a major obstacle for training high-quality machine translation systems for low-resource languages. Fortunately, some low-resource languages are linguistically related or similar to high-resource languages; these related languages may share many lexical or syntactic structures. In this work, we exploit this linguistic overlap to facilitate translating to and from a low-resource language with only monolingual data, in addition to any parallel data in the related high-resource language. Our method, NMT-Adapt, combines denoising autoencoding, back-translation and adversarial objectives to utilize monolingual data for low-resource adaptation. We experiment on 7 languages from three different language families and show that our technique significantly improves translation into low-resource language compared to other translation baselines.",
}