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2019 ACL ACL 2019

The Unbearable Weight of Generating Artificial Errors for Grammatical Error Correction

Abstract

AbstractIn this paper, we investigate the impact of using 4 recent neural models for generating artificial errors to help train the neural grammatical error correction models. We conduct a battery of experiments on the effect of data size, models, and comparison with a rule-based approach.

🌉 Interdisciplinary Bridge — Interdisciplinary and Machine Learning
🧭 Keyword Pioneer — artificial error
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Phu Mon Htut , Joel Tetreault

Topics

Machine Learning > Core Methods > Classification Interdisciplinary > Linguistics > Computational Linguistics

Keywords

neural network training data augmentation grammatical error correction neural model rule-based approach artificial error
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