1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.CL2021
Exploring the Capacity of a Large-scale Masked Language Model to Recognize Grammatical Errors
Ryo Nagata, Manabu Kimura, Kazuaki Hanawa
In this paper, we explore the capacity of a language model-based method for grammatical error detection in detail. We first show that 5 to 10% of training data are enough for a BER…
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…