@inproceedings{cao-etal-2020-integrating,
title = "Integrating {BERT} and Score-based Feature Gates for {C}hinese Grammatical Error Diagnosis",
author = "Cao, Yongchang and
He, Liang and
Ridley, Robert and
Dai, Xinyu",
editor = "YANG, Erhong and
XUN, Endong and
ZHANG, Baolin and
RAO, Gaoqi",
booktitle = "Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.nlptea-1.7",
pages = "49--56",
abstract = "This paper describes our proposed model for the Chinese Grammatical Error Diagnosis (CGED) task in NLPTEA2020. The goal of CGED is to use natural language processing techniques to automatically diagnose Chinese grammatical errors in sentences. To this end, we design and implement a CGED model named BERT with Score-feature Gates Error Diagnoser (BSGED), which is based on the BERT model, Bidirectional Long Short-Term Memory (BiLSTM) and conditional random field (CRF). In order to address the problem of losing partial-order relationships when embedding continuous feature items as with previous works, we propose a gating mechanism for integrating continuous feature items, which effectively retains the partial-order relationships between feature items. We perform LSTM processing on the encoding result of the BERT model, and further extract the sequence features. In the final test-set evaluation, we obtained the highest F1 score at the detection level and are among the top 3 F1 scores at the identification level.",
}
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<abstract>This paper describes our proposed model for the Chinese Grammatical Error Diagnosis (CGED) task in NLPTEA2020. The goal of CGED is to use natural language processing techniques to automatically diagnose Chinese grammatical errors in sentences. To this end, we design and implement a CGED model named BERT with Score-feature Gates Error Diagnoser (BSGED), which is based on the BERT model, Bidirectional Long Short-Term Memory (BiLSTM) and conditional random field (CRF). In order to address the problem of losing partial-order relationships when embedding continuous feature items as with previous works, we propose a gating mechanism for integrating continuous feature items, which effectively retains the partial-order relationships between feature items. We perform LSTM processing on the encoding result of the BERT model, and further extract the sequence features. In the final test-set evaluation, we obtained the highest F1 score at the detection level and are among the top 3 F1 scores at the identification level.</abstract>
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%0 Conference Proceedings
%T Integrating BERT and Score-based Feature Gates for Chinese Grammatical Error Diagnosis
%A Cao, Yongchang
%A He, Liang
%A Ridley, Robert
%A Dai, Xinyu
%Y YANG, Erhong
%Y XUN, Endong
%Y ZHANG, Baolin
%Y RAO, Gaoqi
%S Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications
%D 2020
%8 December
%I Association for Computational Linguistics
%C Suzhou, China
%F cao-etal-2020-integrating
%X This paper describes our proposed model for the Chinese Grammatical Error Diagnosis (CGED) task in NLPTEA2020. The goal of CGED is to use natural language processing techniques to automatically diagnose Chinese grammatical errors in sentences. To this end, we design and implement a CGED model named BERT with Score-feature Gates Error Diagnoser (BSGED), which is based on the BERT model, Bidirectional Long Short-Term Memory (BiLSTM) and conditional random field (CRF). In order to address the problem of losing partial-order relationships when embedding continuous feature items as with previous works, we propose a gating mechanism for integrating continuous feature items, which effectively retains the partial-order relationships between feature items. We perform LSTM processing on the encoding result of the BERT model, and further extract the sequence features. In the final test-set evaluation, we obtained the highest F1 score at the detection level and are among the top 3 F1 scores at the identification level.
%U https://aclanthology.org/2020.nlptea-1.7
%P 49-56
Markdown (Informal)
[Integrating BERT and Score-based Feature Gates for Chinese Grammatical Error Diagnosis](https://aclanthology.org/2020.nlptea-1.7) (Cao et al., NLP-TEA 2020)
ACL