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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.

🌉 Interdisciplinary Bridge — Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — synthetic corpus
🐣 Hot Topic Early Bird — unsupervised learning
🐝 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

Authors

Satoru Katsumata , Mamoru Komachi

Topics

Machine Learning > Learning Types > Unsupervised Learning Natural Language Processing > Generation > Text Generation Natural Language Processing > Applications > Text Generation Machine Learning > Learning Paradigms > Unsupervised Learning Deep Learning > Learning Types > Unsupervised Learning Natural Language Processing > Applications > Grammatical Error Correction

Keywords

unsupervised learning grammatical error correction statistical machine translation phrase-based translation synthetic corpus text correction
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