@inproceedings{junyi-etal-2022-dynamic,
title = "Dynamic Negative Example Construction for Grammatical Error Correction using Contrastive Learning",
author = "Junyi, He and
Junbin, Zhuang and
Xia, Li",
editor = "Sun, Maosong and
Liu, Yang and
Che, Wanxiang and
Feng, Yang and
Qiu, Xipeng and
Rao, Gaoqi and
Chen, Yubo",
booktitle = "Proceedings of the 21st Chinese National Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Nanchang, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2022.ccl-1.83/",
pages = "945--957",
language = "eng",
abstract = "{\textquotedblleft}Grammatical error correction (GEC) aims at correcting texts with different types of grammatical errors into natural and correct forms. Due to the difference of error type distribution and error density, current grammatical error correction systems may over-correct writings and produce a low precision. To address this issue, in this paper, we propose a dynamic negative example construction method for grammatical error correction using contrastive learning. The proposed method can construct sufficient negative examples with diverse grammatical errors, and can be dynamically used during model training. The constructed negative examples are beneficial for the GEC model to correct sentences precisely and suppress the model from over-correction. Experimental results show that our proposed method enhances model precision, proving the effectiveness of our method.{\textquotedblright}"
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%0 Conference Proceedings
%T Dynamic Negative Example Construction for Grammatical Error Correction using Contrastive Learning
%A Junyi, He
%A Junbin, Zhuang
%A Xia, Li
%Y Sun, Maosong
%Y Liu, Yang
%Y Che, Wanxiang
%Y Feng, Yang
%Y Qiu, Xipeng
%Y Rao, Gaoqi
%Y Chen, Yubo
%S Proceedings of the 21st Chinese National Conference on Computational Linguistics
%D 2022
%8 October
%I Chinese Information Processing Society of China
%C Nanchang, China
%G eng
%F junyi-etal-2022-dynamic
%X “Grammatical error correction (GEC) aims at correcting texts with different types of grammatical errors into natural and correct forms. Due to the difference of error type distribution and error density, current grammatical error correction systems may over-correct writings and produce a low precision. To address this issue, in this paper, we propose a dynamic negative example construction method for grammatical error correction using contrastive learning. The proposed method can construct sufficient negative examples with diverse grammatical errors, and can be dynamically used during model training. The constructed negative examples are beneficial for the GEC model to correct sentences precisely and suppress the model from over-correction. Experimental results show that our proposed method enhances model precision, proving the effectiveness of our method.”
%U https://aclanthology.org/2022.ccl-1.83/
%P 945-957
Markdown (Informal)
[Dynamic Negative Example Construction for Grammatical Error Correction using Contrastive Learning](https://aclanthology.org/2022.ccl-1.83/) (Junyi et al., CCL 2022)
ACL