@inproceedings{ikeda-etal-2016-japanese,
    title = "{J}apanese Text Normalization with Encoder-Decoder Model",
    author = "Ikeda, Taishi  and
      Shindo, Hiroyuki  and
      Matsumoto, Yuji",
    editor = "Han, Bo  and
      Ritter, Alan  and
      Derczynski, Leon  and
      Xu, Wei  and
      Baldwin, Tim",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/W16-3918/",
    pages = "129--137",
    abstract = "Text normalization is the task of transforming lexical variants to their canonical forms. We model the problem of text normalization as a character-level sequence to sequence learning problem and present a neural encoder-decoder model for solving it. To train the encoder-decoder model, many sentences pairs are generally required. However, Japanese non-standard canonical pairs are scarce in the form of parallel corpora. To address this issue, we propose a method of data augmentation to increase data size by converting existing resources into synthesized non-standard forms using handcrafted rules. We conducted an experiment to demonstrate that the synthesized corpus contributes to stably train an encoder-decoder model and improve the performance of Japanese text normalization."
}