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arXiv · 1907.09724

Towards Unsupervised Grammatical Error Correction using Statistical Machine Translation with Synthetic Comparable Corpus

Satoru Katsumata·Mamoru Komachi
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Abstract

We introduce unsupervised techniques based on phrase-based statistical machine translation for grammatical error correction (GEC) trained on a pseudo learner corpus created by Google Translation. We verified our GEC system through experiments on various GEC dataset, includi ng a low resource track of the shared task at Building Educational Applications 2019 (BEA 2019). As a result, we achieved an F_0.5 score of 28.31 points with the test data of the low resource track.

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Satoru Katsumata, Mamoru Komachi. 2019-07-23. Towards Unsupervised Grammatical Error Correction using Statistical Machine Translation with Synthetic Comparable Corpus. https://arxiv.org/abs/1907.09724

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