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ProQE: Proficiency-wise Quality Estimation dataset for Grammatical Error Correction
This study investigates how supervised quality estimation (QE) models of grammatical error correction (GEC) are affected by the learners’ proficiency with the data.
Construction of a Quality Estimation Dataset for Automatic Evaluation of Japanese Grammatical Error Correction
Therefore, in this study, we created a quality estimation dataset with manual evaluation to build an automatic evaluation model for Japanese GEC.
Proficiency Matters Quality Estimation in Grammatical Error Correction
This study investigates how supervised quality estimation (QE) models of grammatical error correction (GEC) are affected by the learners' proficiency with the data.
Grammatical Error Correction Using Pseudo Learner Corpus Considering Learner's Error Tendency
To address the limitations of language and computational resources, we assume that introducing pseudo errors into sentences similar to those written by the language learners is more efficient, rather than incorporating random pseudo errors into monolingual data.
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