@inproceedings{kumar-etal-2020-unsupervised,
    title = "Unsupervised Approach for Zero-Shot Experiments: {B}hojpuri{--}{H}indi and {M}agahi{--}{H}indi@{L}o{R}es{MT} 2020",
    author = "Kumar, Amit  and
      Mundotiya, Rajesh Kumar  and
      Singh, Anil Kumar",
    editor = "Karakanta, Alina  and
      Ojha, Atul Kr.  and
      Liu, Chao-Hong  and
      Abbott, Jade  and
      Ortega, John  and
      Washington, Jonathan  and
      Oco, Nathaniel  and
      Lakew, Surafel Melaku  and
      Pirinen, Tommi A  and
      Malykh, Valentin  and
      Logacheva, Varvara  and
      Zhao, Xiaobing",
    booktitle = "Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.loresmt-1.6/",
    doi = "10.18653/v1/2020.loresmt-1.6",
    pages = "43--46",
    abstract = "This paper reports a Machine Translation (MT) system submitted by the NLPRL team for the Bhojpuri{--}Hindi and Magahi{--}Hindi language pairs at LoResMT 2020 shared task. We used an unsupervised domain adaptation approach that gives promising results for zero or extremely low resource languages. Task organizers provide the development and the test sets for evaluation and the monolingual data for training. Our approach is a hybrid approach of domain adaptation and back-translation. Metrics used to evaluate the trained model are BLEU, RIBES, Precision, Recall and F-measure. Our approach gives relatively promising results, with a wide range, of 19.5, 13.71, 2.54, and 3.16 BLEU points for Bhojpuri to Hindi, Magahi to Hindi, Hindi to Bhojpuri and Hindi to Magahi language pairs, respectively."
}