@inproceedings{chada-2020-simultaneous,
    title = "Simultaneous paraphrasing and translation by fine-tuning Transformer models",
    author = "Chada, Rakesh",
    editor = "Birch, Alexandra  and
      Finch, Andrew  and
      Hayashi, Hiroaki  and
      Heafield, Kenneth  and
      Junczys-Dowmunt, Marcin  and
      Konstas, Ioannis  and
      Li, Xian  and
      Neubig, Graham  and
      Oda, Yusuke",
    booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2020.ngt-1.23/",
    doi = "10.18653/v1/2020.ngt-1.23",
    pages = "198--203",
    abstract = "This paper describes the third place submission to the shared task on simultaneous translation and paraphrasing for language education at the 4th workshop on Neural Generation and Translation (WNGT) for ACL 2020. The final system leverages pre-trained translation models and uses a Transformer architecture combined with an oversampling strategy to achieve a competitive performance. This system significantly outperforms the baseline on Hungarian (27{\%} absolute improvement in Weighted Macro F1 score) and Portuguese (33{\%} absolute improvement) languages."
}