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Spelling error correction is an important yet challenging task because a satisfactory solution of it essentially needs human-level language understanding ability.
Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V Le. 2019 · 1906
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Bruno Martins and Mário J. Silva. 2004 · 2004
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A large scale ranker-based system for search query spelling correction
Jianfeng Gao, Xiaolong Li, Daniel Micol, Chris Quirk, and Xu Sun. 2010 · 2010
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Chinese spelling error detection and correction based on language model, pronunciation, and shape
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Yuen-Hsien Tseng, Lung-Hao Lee, Li-Ping Chang, and Hsin-Hsi Chen. 2015 · 2015
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Haithem Afli, Zhengwei Qiu, Andy Way, and Páraic Sheridan. 2016 · 2016
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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A hybrid approach to automatic corpus generation for chinese spelling check
Dingmin Wang, Yan Song, Jing Li, Jialong Han, and Haisong Zhang. 2018b · 2018
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A spelling correction model for end-to-end speech recognition
Jinxi Guo, Tara N Sainath, and Ron J Weiss. 2019 · 2019
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Faspell: A fast, adaptable, simple, powerful chinese spell checker based on dae-decoder paradigm
Yuzhong Hong, Xianguo Yu, Neng He, Nan Liu, and Junhui Liu. 2019 · 2019
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Confusionset-guided pointer networks for chinese spelling check
Dingmin Wang, Yi Tay, and Li Zhong. 2019 · 2019
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Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. 2018a
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