17 citations · 55 across the 15 of their papers we have counts for
Showing 2019Show all
3 papers · 1 filter
cs.CL2019★ 17 cited
GitHub Typo Corpus: A Large-Scale Multilingual Dataset of Misspellings and Grammatical Errors
Masato Hagiwara, Masato Mita
The lack of large-scale datasets has been a major hindrance to the development of NLP tasks such as spelling correction and grammatical error correction (GEC). As a complementary n…
cs.CL2019★ 9 cited
An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction
Shun Kiyono, Jun Suzuki, Masato Mita +2
The incorporation of pseudo data in the training of grammatical error correction models has been one of the main factors in improving the performance of such models. However, conse…
cs.CL2019★ 1 cited
Cross-Corpora Evaluation and Analysis of Grammatical Error Correction Models --- Is Single-Corpus Evaluation Enough?
Masato Mita, Tomoya Mizumoto, Masahiro Kaneko +2
This study explores the necessity of performing cross-corpora evaluation for grammatical error correction (GEC) models. GEC models have been previously evaluated based on a single…