MASATO MITA
Title
Cited by
Cited by
Year
An empirical study of incorporating pseudo data into grammatical error correction
S Kiyono, J Suzuki, M Mita, T Mizumoto, K Inui
arXiv preprint arXiv:1909.00502, 2019
392019
Encoder-decoder models can benefit from pre-trained masked language models in grammatical error correction
M Kaneko, M Mita, S Kiyono, J Suzuki, K Inui
arXiv preprint arXiv:2005.00987, 2020
172020
The AIP-Tohoku system at the BEA-2019 shared task
H Asano, M Mita, T Mizumoto, J Suzuki
Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building …, 2019
92019
Cross-Corpora Evaluation and Analysis of Grammatical Error Correction Models---Is Single-Corpus Evaluation Enough?
M Mita, T Mizumoto, M Kaneko, R Nagata, K Inui
arXiv preprint arXiv:1904.02927, 2019
52019
Github typo corpus: A large-scale multilingual dataset of misspellings and grammatical errors
M Hagiwara, M Mita
arXiv preprint arXiv:1911.12893, 2019
32019
Preventing Critical Scoring Errors in Short Answer Scoring with Confidence Estimation
H Funayama, S Sasaki, Y Matsubayashi, T Mizumoto, J Suzuki, M Mita, ...
Proceedings of the 58th Annual Meeting of the Association for Computational …, 2020
12020
Grammatical Error Correction Considering Multi-word Expressions
T Mizumoto, M Mita, Y Matsumoto
Proceedings of the 2nd Workshop on Natural Language Processing Techniques …, 2015
12015
Taking the Correction Difficulty into Account in Grammatical Error Correction Evaluation
T Gotou, R Nagata, M Mita, K Hanawa
Proceedings of the 28th International Conference on Computational …, 2020
2020
PheMT: A Phenomenon-wise Dataset for Machine Translation Robustness on User-Generated Contents
R Fujii, M Mita, K Abe, K Hanawa, M Morishita, J Suzuki, K Inui
arXiv preprint arXiv:2011.02121, 2020
2020
A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction
M Mita, S Kiyono, M Kaneko, J Suzuki, K Inui
arXiv preprint arXiv:2010.03155, 2020
2020
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Articles 1–10