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Masato Mita

An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction
2019 · Shun Kiyono, Jun Suzuki, et al. · 135
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Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction
2020 · Masahiro Kaneko, Masato Mita, et al. · Annual Meeting of the Association for Computational Linguistics (ACL) · 122
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The AIP-Tohoku System at the BEA-2019 Shared Task
2019 · Hiroki Asano, Masato Mita, et al. · 19
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GitHub Typo Corpus: A Large-Scale Multilingual Dataset of Misspellings and Grammatical Errors
2019 · Hagiwara, Masato, Mita, Masato · arXiv (Cornell University) · 18
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Cross-Corpora Evaluation and Analysis of Grammatical Error Correction Models — Is Single-Corpus Evaluation Enough?
2019 · Masato Mita, Tomoya Mizumoto, et al. · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · 15
A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction
2020 · Masato Mita, Shun Kiyono, et al. · 12
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Preventing Critical Scoring Errors in Short Answer Scoring with Confidence Estimation
2020 · Hiroaki Funayama, Shota Sasaki, et al. · Annual Meeting of the Association for Computational Linguistics: Student Research Workshop (ACL SRW) · 11
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Revisiting Meta-evaluation for Grammatical Error Correction
2024 · Masamune Kobayashi, Masato Mita, et al. · Transactions of the Association for Computational Linguistics · 9
Chinese Grammatical Error Correction Using Pre-trained Models and Pseudo Data
2022 · Hongfei Wang, Michiki Kurosawa, et al. · ACM Transactions on Asian and Low-Resource Language Information Processing · 9
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Shared Task on Feedback Comment Generation for Language Learners
2021 · Ryo Nagata, Masato Hagiwara, et al. · International Conference on Natural Language Generation · 9
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Taking the Correction Difficulty into Account in Grammatical Error Correction Evaluation
2020 · Takumi Gotou, Ryo Nagata, et al. · International Conference on Computational Linguistics · 9
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Striking Gold in Advertising: Standardization and Exploration of Ad Text Generation
2024 · Masato Mita, Soichiro Murakami, et al. · Annual Meeting of the Association for Computational Linguistics (ACL) · 8
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Do Grammatical Error Correction Models Realize Grammatical Generalization?
2021 · Masato Mita, Hitomi Yanaka · 8
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PheMT: A Phenomenon-wise Dataset for Machine Translation Robustness on User-Generated Contents
2020 · Ryo Fujii, Masato Mita, et al. · International Conference on Computational Linguistics · 7
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An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction
2019 · Kiyono, Shun, Suzuki, Jun, et al. · arXiv (Cornell University) · 6
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Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction
2020 · Kaneko, Masahiro, Mita, Masato, et al. · arXiv (Cornell University) · 5
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Construction of a Quality Estimation Dataset for Automatic Evaluation of Japanese Grammatical Error Correction
2022 · Daisuke Suzuki, Yujin Takahashi, et al. · Language Resources and Evaluation Conference (LREC) · 4
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Japanese Lexical Complexity for Non-Native Readers: A New Dataset
2023 · Yusuke Ide, Masato Mita, et al. · Workshop on Innovative Use of NLP for Building Educational Applications · 3
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Cloze Quality Estimation for Language Assessment
2023 · Zizheng Zhang, Masato Mita, et al. · Findings of the Association for Computational Linguistics: EACL · 3
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Towards Automated Document Revision: Grammatical Error Correction, Fluency Edits, and Beyond
2022 · Mita, Masato, Sakaguchi, Keisuke, et al. · arXiv (Cornell University) · 3
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Do Grammatical Error Correction Models Realize Grammatical Generalization?
2021 · Mita, Masato, Yanaka, Hitomi · arXiv (Cornell University) · 3
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Developmentally-plausible Working Memory Shapes a Critical Period for Language Acquisition
2025 · Masato Mita, Ryo Yoshida, et al. · Annual Meeting of the Association for Computational Linguistics (ACL) · 2
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Token-length Bias in Minimal-pair Paradigm Datasets
2024 · Naoya Ueda, Masato Mita, et al. · Language Resources and Evaluation Conference (LREC) · 2
Large Language Models Are State-of-the-Art Evaluator for Grammatical Error Correction
2024 · Kobayashi, Masamune, Mita, Masato, et al. · arXiv (Cornell University) · 2
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Cloze Quality Estimation for Language Assessment
2024 · Zizheng Zhang, Masato Mita, et al. · Journal of Natural Language Processing · 2
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Works count: 73
Citation count: 441
H-index: 9
I10-index: 7
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