17 citations · 37 across the 7 of their papers we have counts for
9 papers · 1 filter
Towards Automated Document Revision: Grammatical Error Correction, Fluency Edits, and Beyond
Masato Mita, Keisuke Sakaguchi, Masato Hagiwara +3
Natural language processing technology has rapidly improved automated grammatical error correction tasks, and the community begins to explore document-level revision as one of the…
Construction of a Quality Estimation Dataset for Automatic Evaluation of Japanese Grammatical Error Correction
Daisuke Suzuki, Yujin Takahashi, Ikumi Yamashita +5
In grammatical error correction (GEC), automatic evaluation is an important factor for research and development of GEC systems. Previous studies on automatic evaluation have demons…
Proficiency Matters Quality Estimation in Grammatical Error Correction
Yujin Takahashi, Masahiro Kaneko, Masato Mita +1
This study investigates how supervised quality estimation (QE) models of grammatical error correction (GEC) are affected by the learners' proficiency with the data. QE models for G…
Do Grammatical Error Correction Models Realize Grammatical Generalization?
Masato Mita, Hitomi Yanaka
There has been an increased interest in data generation approaches to grammatical error correction (GEC) using pseudo data. However, these approaches suffer from several issues tha…
A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction
Masato Mita, Shun Kiyono, Masahiro Kaneko +2
Existing approaches for grammatical error correction (GEC) largely rely on supervised learning with manually created GEC datasets. However, there has been little focus on verifying…
Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction
Masahiro Kaneko, Masato Mita, Shun Kiyono +2
This paper investigates how to effectively incorporate a pre-trained masked language model (MLM), such as BERT, into an encoder-decoder (EncDec) model for grammatical error correct…