(Almost) Unsupervised Grammatical Error Correction using Synthetic Comparable Corpus
説明
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 a low resource track of the shared task at BEA2019. As a result, we achieved an F0.5 score of 28.31 points with the test data.
収録刊行物
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- Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
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Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications 134-138, 2019-01-01
Association for Computational Linguistics (ACL)
