@inproceedings{poncelas-htun-2022-controlling,
    title = "Controlling {J}apanese Machine Translation Output by Using {JLPT} Vocabulary Levels",
    author = "Poncelas, Alberto  and
      Htun, Ohnmar",
    editor = "{\v{S}}tajner, Sanja  and
      Saggion, Horacio  and
      Ferr{\'e}s, Daniel  and
      Shardlow, Matthew  and
      Sheang, Kim Cheng  and
      North, Kai  and
      Zampieri, Marcos  and
      Xu, Wei",
    booktitle = "Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/2022.tsar-1.7/",
    doi = "10.18653/v1/2022.tsar-1.7",
    pages = "77--85",
    abstract = "In Neural Machine Translation (NMT) systems, there is generally little control over the lexicon of the output. Consequently, the translated output may be too difficult for certain audiences. For example, for people with limited knowledge of the language, vocabulary is a major impediment to understanding a text. In this work, we build a complexity-controllable NMT for English-to-Japanese translations. More particularly, we aim to modulate the difficulty of the translation in terms of not only the vocabulary but also the use of kanji. For achieving this, we follow a sentence-tagging approach to influence the output. Controlling Japanese Machine Translation Output by Using JLPT Vocabulary Levels."
}