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        <title>Building a Japanese Typo Dataset from Wikipedia’s Revision History</title>
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            <namePart type="given">Shruti</namePart>
            <namePart type="family">Rijhwani</namePart>
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        <name type="personal">
            <namePart type="given">Jiangming</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Building a Japanese Typo Dataset from Wikipedia’s Revision History
%A Tanaka, Yu
%A Murawaki, Yugo
%A Kawahara, Daisuke
%A Kurohashi, Sadao
%Y Rijhwani, Shruti
%Y Liu, Jiangming
%Y Wang, Yizhong
%Y Dror, Rotem
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F tanaka-etal-2020-building
%X 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.
%R 10.18653/v1/2020.acl-srw.31
%U https://aclanthology.org/2020.acl-srw.31
%U https://doi.org/10.18653/v1/2020.acl-srw.31
%P 230-236

Markdown (Informal)

[Building a Japanese Typo Dataset from Wikipedia’s Revision History](https://aclanthology.org/2020.acl-srw.31) (Tanaka et al., ACL 2020)

• Building a Japanese Typo Dataset from Wikipedia’s Revision History (Tanaka et al., ACL 2020)

ACL

• Yu Tanaka, Yugo Murawaki, Daisuke Kawahara, and Sadao Kurohashi. 2020. Building a Japanese Typo Dataset from Wikipedia’s Revision History. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 230–236, Online. Association for Computational Linguistics.