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

2019/07/23 by Satoru Katsumata, Katsumata, Satoru, Mamoru Komachi +1
Computer Science · #Natural Language Processing Techniques #Topic Modeling #Text Readability and Simplification

paper · pdf · doi:10.48550/arxiv.1907.09724

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 F₀.5 score of 28.31 points with the test data of the low resource track.

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