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Papers/Stronger Baselines for Grammatical Error Correction Using ...

Stronger Baselines for Grammatical Error Correction Using Pretrained Encoder-Decoder Model

Satoru Katsumata, Mamoru Komachi

2020-05-24Grammatical Error Correction
PaperPDFCodeCode(official)

Abstract

Studies on grammatical error correction (GEC) have reported the effectiveness of pretraining a Seq2Seq model with a large amount of pseudodata. However, this approach requires time-consuming pretraining for GEC because of the size of the pseudodata. In this study, we explore the utility of bidirectional and auto-regressive transformers (BART) as a generic pretrained encoder-decoder model for GEC. With the use of this generic pretrained model for GEC, the time-consuming pretraining can be eliminated. We find that monolingual and multilingual BART models achieve high performance in GEC, with one of the results being comparable to the current strong results in English GEC. Our implementations are publicly available at GitHub (https://github.com/Katsumata420/generic-pretrained-GEC).

Results

TaskDatasetMetricValueModel
Grammatical Error CorrectionCoNLL-2014 Shared TaskF0.563BART
Grammatical Error CorrectionCoNLL-2014 Shared TaskPrecision69.9BART
Grammatical Error CorrectionCoNLL-2014 Shared TaskRecall45.1BART

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