@inproceedings{haque-etal-2020-adapt,
    title = "The {ADAPT} System Description for the {STAPLE} 2020 {E}nglish-to-{P}ortuguese Translation Task",
    author = "Haque, Rejwanul  and
      Moslem, Yasmin  and
      Way, Andy",
    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/jlcl-multiple-ingestion/2020.ngt-1.17/",
    doi = "10.18653/v1/2020.ngt-1.17",
    pages = "144--152",
    abstract = "This paper describes the ADAPT Centre`s submission to STAPLE (Simultaneous Translation and Paraphrase for Language Education) 2020, a shared task of the 4th Workshop on Neural Generation and Translation (WNGT), for the English-to-Portuguese translation task. In this shared task, the participants were asked to produce high-coverage sets of plausible translations given English prompts (input source sentences). We present our English-to-Portuguese machine translation (MT) models that were built applying various strategies, e.g. data and sentence selection, monolingual MT for generating alternative translations, and combining multiple n-best translations. Our experiments show that adding the aforementioned techniques to the baseline yields an excellent performance in the English-to-Portuguese translation task."
}