Skip to content
conftrace_
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.

🐣 Hot Topic Early Bird - contextual representation

Authors

Jianbo Zhao, Si Li, Zhiqing Lin

Topics

Machine Learning > Core Methods > Representation Learning

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
Download PDF

More from ACL 2018

All ACL 2018 papers →
Learning to Automatically Generate Fill-In-The-Blank Quizzes
Multilingual Short Text Responses Clustering for Mobile Educational Activities: a Preliminary Exploration
Chinese Grammatical Error Diagnosis Based on CRF and LSTM-CRF model
CMMC-BDRC Solution to the NLP-TEA-2018 Chinese Grammatical Error Diagnosis Task
Detecting Simultaneously Chinese Grammar Errors Based on a BiLSTM-CRF Model
A Hybrid Approach Combining Statistical Knowledge with Conditional Random Fields for Chinese Grammatical Error Detection