@inproceedings{poncelas-effendi-2022-benefiting,
    title = "Benefiting from Language Similarity in the Multilingual {MT} Training: Case Study of {I}ndonesian and {M}alaysian",
    author = "Poncelas, Alberto  and
      Effendi, Johanes",
    editor = "Ojha, Atul Kr.  and
      Liu, Chao-Hong  and
      Vylomova, Ekaterina  and
      Abbott, Jade  and
      Washington, Jonathan  and
      Oco, Nathaniel  and
      Pirinen, Tommi A  and
      Malykh, Valentin  and
      Logacheva, Varvara  and
      Zhao, Xiaobing",
    booktitle = "Proceedings of the Fifth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2022)",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/moar-dois/2022.loresmt-1.11/",
    pages = "84--92",
    abstract = "The development of machine translation (MT) has been successful in breaking the language barrier of the world{'}s top 10-20 languages. However, for the rest of it, delivering an acceptable translation quality is still a challenge due to the limited resource. To tackle this problem, most studies focus on augmenting data while overlooking the fact that we can borrow high-quality natural data from the closely-related language. In this work, we propose an MT model training strategy by increasing the language directions as a means of augmentation in a multilingual setting. Our experiment result using Indonesian and Malaysian on the state-of-the-art MT model showcases the effectiveness and robustness of our method."
}