SpellGCN is a method for chinese spelling check, which embeds the visual and phological knowledge into BERT. This repository contains data, evaluation and training scripts.
Citation:
@inproceedings{DBLP:journals/corr/abs-2004-14166,
author = {Xingyi Cheng and
Weidi Xu and
Kunlong Chen and
Shaohua Jiang and
Feng Wang and
Taifeng Wang and
Wei Chu and
Yuan Qi},
title={SpellGCN: Incorporating Phonological and Visual Similarities into
Language Models for Chinese Spelling Check},
booktitle={ACL},
year={2020}
}
This is the official code for paper titled "SpellGCN: Incorporating Phonological and Visual Similarities into Chinese Spelling Check".
The code is based on Tensorflow==1.13.1 and python 2.7 or higher
Run commands as follows:
cd scripts/
conda create -n spellgcn python=2.7.1
source activate spellgcn
pip install tensorflow==1.13.1
sh run.sh
Note: Since SpellGCN is based on BERT, the path to the BERT directory should be provided in the run.sh. The default training data is the combination of data samples from SIGHAN13, SIGHAN14, SIGHAN15. The additional 270K data samples are absent here due to the lack of permission.