@inproceedings{khatri-etal-2021-language,
    title = "Language Relatedness and Lexical Closeness can help Improve Multilingual {NMT}: {IITB}ombay@{M}ulti{I}ndic{NMT} {WAT}2021",
    author = "Khatri, Jyotsana  and
      Saini, Nikhil  and
      Bhattacharyya, Pushpak",
    editor = "Nakazawa, Toshiaki  and
      Nakayama, Hideki  and
      Goto, Isao  and
      Mino, Hideya  and
      Ding, Chenchen  and
      Dabre, Raj  and
      Kunchukuttan, Anoop  and
      Higashiyama, Shohei  and
      Manabe, Hiroshi  and
      Pa, Win Pa  and
      Parida, Shantipriya  and
      Bojar, Ond{\v{r}}ej  and
      Chu, Chenhui  and
      Eriguchi, Akiko  and
      Abe, Kaori  and
      Oda, Yusuke  and
      Sudoh, Katsuhito  and
      Kurohashi, Sadao  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 8th Workshop on Asian Translation (WAT2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2021.wat-1.26/",
    doi = "10.18653/v1/2021.wat-1.26",
    pages = "217--223",
    abstract = "Multilingual Neural Machine Translation has achieved remarkable performance by training a single translation model for multiple languages. This paper describes our submission (Team ID: CFILT-IITB) for the MultiIndicMT: An Indic Language Multilingual Task at WAT 2021. We train multilingual NMT systems by sharing encoder and decoder parameters with language embedding associated with each token in both encoder and decoder. Furthermore, we demonstrate the use of transliteration (script conversion) for Indic languages in reducing the lexical gap for training a multilingual NMT system. Further, we show improvement in performance by training a multilingual NMT system using languages of the same family, i.e., related languages."
}