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J-GLOBAL ID:202102253520327787   Reference number:21A0059196

Multi-dialect Neural Machine Translation for 48 Low-resource Japanese Dialects

48低リソース日本語方言のための多方言ニューラル機械翻訳
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Volume: 27  Issue:Page: 781-800  Publication year: Dec. 15, 2020 
JST Material Number: L3934A  ISSN: 1340-7619  Document type: Article
Article type: 原著論文  Country of issue: Japan (JPN)  Language: ENGLISH (EN)
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Natural language processing  ,  Neurocomputers 
Reference (26):
  • Arivazhagan, N., Bapna, A., Firat, O., Lepikhin, D., Johnson, M. G., Krikun, M., Chen, M. X., Cao, Y., Foster, G., Cherry, C., Macherey, W., Chen, Z., and Wu, Y. (2019). “Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges.” arXiv preprint arXiv:1907.05019.
  • Gu, J., Hassan, H., Devlin, J., and Li, V. O. (2018). “Universal Neural Machine Translation for Extremely Low Resource Languages.” In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pp. 185-194. Association for Computational Linguistics.
  • Guggilla, C. (2016). “Discrimination Between Similar Languages, Varieties and Dialects Using CNN- and LSTM-based Deep Neural Networks.” In Proceedings of the 3rd Workshop on NLP for Similar Languages, Varieties and Dialects, pp. 186-194. The COLING 2016 Organizing Committee.
  • Gülçehre, Ç., Ahn, S., Nallapati, R., Zhou, B., and Bengio, Y. (2016). “Pointing the Unknown Words.” CoRR, abs/1603.08148.
  • Hassan, H., Elaraby, M., and Tawfik, A. Y. (2017). “Synthetic Data for Neural Machine Translation of Spoken-Dialects.” arXiv preprint arXiv:1707.00079.
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