2019
ACL
ACL 2019
(Almost) Unsupervised Grammatical Error Correction using Synthetic Comparable Corpus
Abstract
AbstractWe 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.
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Interdisciplinary Bridge
- Machine Learning and Natural Language Processing
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Keyword Pioneer
- synthetic corpus
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Hot Topic Early Bird
- unsupervised learning
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Cross-Pollinator
- Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio