Yu Tanaka


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Building a Japanese Typo Dataset from Wikipedia’s Revision History
Yu Tanaka | Yugo Murawaki | Daisuke Kawahara | Sadao Kurohashi
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

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.

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A System for Worldwide COVID-19 Information Aggregation
Akiko Aizawa | Frederic Bergeron | Junjie Chen | Fei Cheng | Katsuhiko Hayashi | Kentaro Inui | Hiroyoshi Ito | Daisuke Kawahara | Masaru Kitsuregawa | Hirokazu Kiyomaru | Masaki Kobayashi | Takashi Kodama | Sadao Kurohashi | Qianying Liu | Masaki Matsubara | Yusuke Miyao | Atsuyuki Morishima | Yugo Murawaki | Kazumasa Omura | Haiyue Song | Eiichiro Sumita | Shinji Suzuki | Ribeka Tanaka | Yu Tanaka | Masashi Toyoda | Nobuhiro Ueda | Honai Ueoka | Masao Utiyama | Ying Zhong
Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020

The global pandemic of COVID-19 has made the public pay close attention to related news, covering various domains, such as sanitation, treatment, and effects on education. Meanwhile, the COVID-19 condition is very different among the countries (e.g., policies and development of the epidemic), and thus citizens would be interested in news in foreign countries. We build a system for worldwide COVID-19 information aggregation containing reliable articles from 10 regions in 7 languages sorted by topics. Our reliable COVID-19 related website dataset collected through crowdsourcing ensures the quality of the articles. A neural machine translation module translates articles in other languages into Japanese and English. A BERT-based topic-classifier trained on our article-topic pair dataset helps users find their interested information efficiently by putting articles into different categories.