@inproceedings{tanaka-etal-2020-building,
    title = "Building a {J}apanese Typo Dataset from {W}ikipedia`s Revision History",
    author = "Tanaka, Yu  and
      Murawaki, Yugo  and
      Kawahara, Daisuke  and
      Kurohashi, Sadao",
    editor = "Rijhwani, Shruti  and
      Liu, Jiangming  and
      Wang, Yizhong  and
      Dror, Rotem",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/2020.acl-srw.31/",
    doi = "10.18653/v1/2020.acl-srw.31",
    pages = "230--236",
    abstract = "User generated texts contain many typos for which correction is necessary for NLP systems to work. Although a large number of typo{--}correction pairs are needed to develop a data-driven typo correction system, no such dataset is available for Japanese. In this paper, we extract over half a million Japanese typo{--}correction pairs from Wikipedia`s revision history. Unlike other languages, Japanese poses unique challenges: (1) Japanese texts are unsegmented so that we cannot simply apply a spelling checker, and (2) the way people inputting kanji logographs results in typos with drastically different surface forms from correct ones. We address them by combining character-based extraction rules, morphological analyzers to guess readings, and various filtering methods. We evaluate the dataset using crowdsourcing and run a baseline seq2seq model for typo correction."
}