@inproceedings{ahmadnia-dorr-2019-bilingual,
    title = "Bilingual Low-Resource Neural Machine Translation with Round-Tripping: The Case of {P}ersian-{S}panish",
    author = "Ahmadnia, Benyamin  and
      Dorr, Bonnie",
    editor = "Mitkov, Ruslan  and
      Angelova, Galia",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/R19-1003/",
    doi = "10.26615/978-954-452-056-4_003",
    pages = "18--24",
    abstract = "The quality of Neural Machine Translation (NMT), as a data-driven approach, massively depends on quantity, quality, and relevance of the training dataset. Such approaches have achieved promising results for bilingually high-resource scenarios but are inadequate for low-resource conditions. This paper describes a round-trip training approach to bilingual low-resource NMT that takes advantage of monolingual datasets to address training data scarcity, thus augmenting translation quality. We conduct detailed experiments on Persian-Spanish as a bilingually low-resource scenario. Experimental results demonstrate that this competitive approach outperforms the baselines."
}