2018
ACL
ACL 2018
Contextualized Character Representation for Chinese Grammatical Error Diagnosis
Abstract
AbstractNowadays, more and more people are learning Chinese as their second language. Establishing an automatic diagnosis system for Chinese grammatical error has become an important challenge. In this paper, we propose a Chinese grammatical error diagnosis (CGED) model with contextualized character representation. Compared to the traditional model using LSTM (Long-Short Term Memory), our model have better performance and there is no need to add too many artificial features.
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Keyword Pioneer
— contextualized character representation
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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
Keywords
sequence labeling
contextual representation
long short-term memory
grammatical error diagnosis
chinese language processing
chinese language
grammatical error detection
chinese language learning
chinese grammatical error diagnosis
neural network
contextualized character representation
contextualized character
contextual character representation