@inproceedings{madaan-sadat-2020-multilingual,
    title = "Multilingual Neural Machine Translation involving {I}ndian Languages",
    author = "Madaan, Pulkit  and
      Sadat, Fatiha",
    editor = "Jha, Girish Nath  and
      Bali, Kalika  and
      L., Sobha  and
      Agrawal, S. S.  and
      Ojha, Atul Kr.",
    booktitle = "Proceedings of the WILDRE5{--} 5th Workshop on Indian Language Data: Resources and Evaluation",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.wildre-1.6/",
    pages = "29--32",
    language = "eng",
    ISBN = "979-10-95546-67-2",
    abstract = "Neural Machine Translations (NMT) models are capable of translating a single bilingual pair and require a new model for each new language pair. Multilingual Neural Machine Translation models are capable of translating multiple language pairs, even pairs which it hasn{'}t seen before in training. Availability of parallel sentences is a known problem in machine translation. Multilingual NMT model leverages information from all the languages to improve itself and performs better. We propose a data augmentation technique that further improves this model profoundly. The technique helps achieve a jump of more than 15 points in BLEU score from the multilingual NMT model. A BLEU score of 36.2 was achieved for Sindhi{--}English translation, which is higher than any score on the leaderboard of the LoResMT SharedTask at MT Summit 2019, which provided the data for the experiments."
}