@inproceedings{xie-2021-gx,
    title = "{GX}@{D}ravidian{L}ang{T}ech-{EACL}2021: Multilingual Neural Machine Translation and Back-translation",
    author = "Xie, Wanying",
    editor = "Chakravarthi, Bharathi Raja  and
      Priyadharshini, Ruba  and
      Kumar M, Anand  and
      Krishnamurthy, Parameswari  and
      Sherly, Elizabeth",
    booktitle = "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages",
    month = apr,
    year = "2021",
    address = "Kyiv",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.dravidianlangtech-1.18/",
    pages = "146--153",
    abstract = "In this paper, we describe the GX system in the EACL2021 shared task on machine translation in Dravidian languages. Given the low amount of parallel training data, We adopt two methods to improve the overall performance: (1) multilingual translation, we use a shared encoder-decoder multilingual translation model handling multiple languages simultaneously to facilitate the translation performance of these languages; (2) back-translation, we collected other open-source parallel and monolingual data and apply back-translation to benefit from the monolingual data. The experimental results show that we can achieve satisfactory translation results in these Dravidian languages and rank first in English-Telugu and Tamil-Telugu translation."
}