2020
ACL
ACL 2020
A Comparative Study of Synthetic Data Generation Methods for Grammatical Error Correction
Abstract
AbstractGrammatical Error Correction (GEC) is concerned with correcting grammatical errors in written text. Current GEC systems, namely those leveraging statistical and neural machine translation, require large quantities of annotated training data, which can be expensive or impractical to obtain. This research compares techniques for generating synthetic data utilized by the two highest scoring submissions to the restricted and low-resource tracks in the BEA-2019 Shared Task on Grammatical Error Correction.
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Interdisciplinary Bridge
— Machine Learning and Natural Language Processing
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Cross-Pollinator
— Artificial Intelligence, Computer Vision, Data Science & Analytics, Deep Learning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio
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Hot Topic Early Bird
— low-resource learning
Authors
Topics
Machine Learning > Learning Types > Weakly Supervised Learning
Natural Language Processing > Generation > Text Generation
Natural Language Processing > Applications > Machine Translation
Machine Learning > Learning Types > Data Augmentation
Deep Learning > Learning Types > Generative Models
Natural Language Processing > Applications > Text Processing