@inproceedings{jain-etal-2021-evaluating,
    title = "Evaluating the Performance of Back-translation for Low Resource {E}nglish-{M}arathi Language Pair: {CFILT}-{IITB}ombay @ {L}o{R}es{MT} 2021",
    author = "Jain, Aditya  and
      Mhaskar, Shivam  and
      Bhattacharyya, Pushpak",
    editor = "Ortega, John  and
      Ojha, Atul Kr.  and
      Kann, Katharina  and
      Liu, Chao-Hong",
    booktitle = "Proceedings of the 4th Workshop on Technologies for MT of Low Resource Languages (LoResMT2021)",
    month = aug,
    year = "2021",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.mtsummit-loresmt.17/",
    pages = "158--162",
    abstract = "In this paper, we discuss the details of the various Machine Translation (MT) systems that we have submitted for the English-Marathi LoResMT task. As a part of this task, we have submitted three different Neural Machine Translation (NMT) systems; a Baseline English-Marathi system, a Baseline Marathi-English system, and an English-Marathi system that is based on the back-translation technique. We explore the performance of these NMT systems between English and Marathi languages, which forms a low resource language pair due to unavailability of sufficient parallel data. We also explore the performance of the back-translation technique when the back-translated data is obtained from NMT systems that are trained on a very less amount of data. From our experiments, we observe that the back-translation technique can help improve the MT quality over the baseline for the English-Marathi language pair."
}