wangwang110/CSC

ChineseBert用于中文拼写纠错

stars

commits

Python

primary language

Mar 14, 2023

updated

Browse cluster: Chinese Spelling Error Correction →

README

CSC

中文拼写纠错

本项目只关注Chinese Spelling Check (CSC)，不考虑多字少字的语法纠错。

关于语法纠错可参考 https://github.com/HillZhang1999/MuCGEC

常用数据来源:

SIGHAN Bake-off 2013: http://ir.itc.ntnu.edu.tw/lre/sighan7csc.html

SIGHAN Bake-off 2014: http://ir.itc.ntnu.edu.tw/lre/clp14csc.html

SIGHAN Bake-off 2015: http://ir.itc.ntnu.edu.tw/lre/sighan8csc.html

Wang271K: https://github.com/wdimmy/Automatic-Corpus-Generation/tree/master/corpus

sighan13，sighan14，sighan15 包含对应的训练集和测试集，Wang271K是论文利用OCR以及语音识别的方法生成的数据构，仅仅用来训练

评价指标

使用句子级纠正F1值。

关于句子级别纠正P值，有两种计算方式

分母不考虑修改了原句但是与正确句子不同的情况
分母考虑修改了原句但是与正确句子不同的情况

使用第一种计算方式得到的结果偏高，这里采用第2种

代码

该指标更为严格（所有修正过的句子都算作P值的分母），论文大都使用该评价指标

榜单

各论文指标（不同的论文使用bert取得的结果不一样,可能是使用的预训练数据不一样或者超参数的设置不同）

sighan13结果:

模型	Detection Level	Correction Level	来源
Hybrid	54.0 69.3 60.7	_ _ 52.1	A Hybrid Approach to Automatic Corpus Generation for Chinese Spelling Check
FASpell	76.2 63.2 69.1	73.1 60.5 66.2	FASPell: A Fast, Adaptable, Simple, Powerful Chinese Spell Checker Based On DAE-Decoder Paradigm
Google BERT	79.0 72.8 75.8	77.7 71.6 74.6	SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check
SpellGCN	80.1 74.4 77.2	78.3 72.7 75.4	SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check
HeadFilt	100.0 74.9 85.7	100.0 74.1 85.1	Domain-shift Conditioning using Adaptable Filtering via Hierarchical Embeddings for Robust Chinese Spell Check
Chunk-based CSC	61.19 75.67 67.66	74.34 67.20 70.59	Chunk-based Chinese Spelling Check with Global Optimization
BERT + Pre-trained for CSC	- - 84.9	- - 84.4	Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models
BERT + Adversarial training	- - 84.0	- - 83.5	Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models
SpellGCN+	85.7 78.8 82.1	84.6 77.8 81.0	Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking
BERT-wwm	85.0 77.0 80.8	83.0 75.2 78.9	Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking
REALISE (BERT-wwm )	88.6 82.5 85.4	887.2 81.2 84.1	Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking
BERT_CRS	84.8 79.5 82.1	83.9 78.7 81.2	Global Attention Decoder for Chinese Spelling Error Correction
BERT_CRS_GAD	85.7 79.5 82.5	84.9 78.7 81.6	Global Attention Decoder for Chinese Spelling Error Correction
ERNIE	76.6 71.9 74.2	73.0 68.5 70.6	Correcting Chinese Spelling Errors with Phonetic Pre-training
MLM-phonetics	82.0 78.3 80.1	79.5 77.0 78.2	Correcting Chinese Spelling Errors with Phonetic Pre-training
RoBERTa	85.4 77.7 81.3	83.9 76.4 79.9	Dynamic Connected Networks for Chinese Spelling Check
RoBERTa-DCN	86.2 78.4 82.1	84.6 76.9 80.5	Dynamic Connected Networks for Chinese Spelling Check
RoBERTa-Pretrain-DCN	86.8 79.6 83.0	84.7 77.7 81.0	Dynamic Connected Networks for Chinese Spelling Check
Google BERT	98.7 70.6 82.3	98.6 67.8 80.4	PHMOSpell: Phonological and Morphological Knowledge Guided Chinese Spelling Check
PHMOSpell	99.5 76.8 86.7	99.5 75.1 85.6	PHMOSpell: Phonological and Morphological Knowledge Guided Chinese Spelling Check
ECOPO BERT-wwm	87.2 81.7 84.4	86.1 80.6 83.3	The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking
ECOPO REALISE (BERT-wwm )	89.3 83.2 86.2	88.5 82.0 85.1	The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking
Google BERT	79.0 72.8 75.8	77.7 71.6 74.6	MDCSpell: A Multi-task Detector-Corrector Framework for Chinese Spelling Correction
MDCSpell	89.1 78.3 83.4	87.5 76.8 81.8	MDCSpell: A Multi-task Detector-Corrector Framework for Chinese Spelling Correction
Soft-Masked BERT	81.1 75.7 78.3	75.1 70.1 72.5	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
CL (Soft-Masked BERT)	84.7 77.0 80.7	80.9 74.5 77.6	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
Google BERT	98.7 70.6 82.3	98.6 67.8 80.4	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
CL (BERT)	99.1 74.8 85.3	99.1 73.2 84.2	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
MacBERT	98.7 70.8 82.5	98.6 67.9 80.4	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
CL (MacBERT)	99.3 75.7 85.9	99.2 73.8 84.6	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
uChecker	75.4 73.4 74.4	72.6 70.8 71.7	uChecker: Masked Pretrained Language Models as Unsupervised Chinese Spelling Checkers
Google BERT	85.0 77.0 80.8	83.0 75.2 78.9	Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking
LEAD	88.3 83.4 85.8	87.2 82.4 84.7	Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking
SCOPE(ChineseBert)	87.4 83.4 85.4	86.3 82.4 84.3	Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity
SCOPE(REALISE)	87.5 83.2 85.3	86.4 82.3 84.3	Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity
SDCL	88.9 81.8 85.2	88.0 81.0 84.3	SDCL: Self-Distillation Contrastive Learning for Chinese Spell Checking
InfoKNN-CSC	89.7 82.8 86.1	82.1 88.7 81.9 85.2	Chinese Spelling Check with Nearest Neighbors

sighan14结果:

模型	Detection Level	Correction Level	来源
Hybrid	51.9 66.2 58.2	_ _ 56.1	A Hybrid Approach to Automatic Corpus Generation for Chinese Spelling Check
FASpell	61.0 53.5 57.0	59.4 52.0 55.4	FASPell: A Fast, Adaptable, Simple, Powerful Chinese Spell Checker Based On DAE-Decoder Paradigm
Google BERT	65.6 68.1 66.8	63.1 65.5 64.3	SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check
SpellGCN	65.1 69.5 67.2	63.1 67.2 65.3	SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check
HeadFilt	82.5 61.6 70.5	82.1 60.2 69.4	Domain-shift Conditioning using Adaptable Filtering via Hierarchical Embeddings for Robust Chinese Spell Check
Chunk-based CSC	78.65 54.80 64.59	77.43 51.04 61.52	Chunk-based Chinese Spelling Check with Global Optimization
BERT + Pre-trained for CSC	- - 70.4	- - 68.6	Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models
BERT + Adversarial training	- - 68.4	- - 66.8	Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models
BERT-wwm	64.5 68.6 66.5	62.4 66.3 64.3	Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking
REALISE (BERT-wwm )	67.8 71.5 69.6	66.3 70.0 68.1	Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking
BERT_CRS	65.4 72.7 68.9	63.4 70.4 66.7	Global Attention Decoder for Chinese Spelling Error Correction
BERT_CRS_GAD	66.6 71.8 69.1	65.0 70.1 67.5	Global Attention Decoder for Chinese Spelling Error Correction
ERNIE	63.5 69.3 66.3	60.1 65.6 62.8	Correcting Chinese Spelling Errors with Phonetic Pre-training
MLM-phonetics	66.2 73.8 69.8	64.2 73.8 68.7	Correcting Chinese Spelling Errors with Phonetic Pre-training
RoBERTa	64.2 68.4 66.2	62.7 66.7 64.6	Dynamic Connected Networks for Chinese Spelling Check
RoBERTa-DCN	67.6 68.6 68.0	64.9 65.9 65.4	Dynamic Connected Networks for Chinese Spelling Check
RoBERTa-Pretrain-DCN	67.4 70.4 68.9	65.8 68.7 67.2	Dynamic Connected Networks for Chinese Spelling Check
Google BERT	78.6 60.7 68.5	77.8 57.6 66.2	PHMOSpell: Phonological and Morphological Knowledge Guided Chinese Spelling Check
PHMOSpell	85.3 67.6 75.5	84.7 64.3 73.1	PHMOSpell: Phonological and Morphological Knowledge Guided Chinese Spelling Check
Google BERT(4 layer)	82.6 59.0 68.8	82.4 58.0 68.1	SpellBERT: A Lightweight Pretrained Model for Chinese Spelling Check
SpellBERT (4 layer)	83.1 62.0 71.0	82.9 61.2 70.4	SpellBERT: A Lightweight Pretrained Model for Chinese Spelling Check
ECOPO BERT-wwm	65.8 69.0 67.4	63.7 66.9 65.3	The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking
ECOPO REALISE (BERT-wwm )	68.8 72.1 70.4	67.5 71.0 69.2	The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking
Google BERT	65.6 68.1 66.8	63.1 65.5 64.3	MDCSpell: A Multi-task Detector-Corrector Framework for Chinese Spelling Correction
MDCSpell	70.2 68.8 69.5	69.0 67.7 68.3	MDCSpell: A Multi-task Detector-Corrector Framework for Chinese Spelling Correction
Soft-Masked BERT	65.2 70.4 67.7	63.7 68.7 66.1	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
CL (Soft-Masked BERT)	68.4 70.9 69.6	67.8 69.1 68.4	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
Google BERT	78.6 60.7 68.5	77.8 57.6 66.2	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
CL (BERT)	79.2 61.6 69.3	78.5 60.8 68.5	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
MacBERT	78.8 61.0 68.8	78.0 58.0 66.5	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
CL (MacBERT)	79.7 62.4 70.0	79.0 61.4 69.1	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
uChecker	61.7 61.5 61.6	57.6 57.5 57.6	uChecker: Masked Pretrained Language Models as Unsupervised Chinese Spelling Checkers
Google BERT	64.5 68.6 66.5	62.4 66.3 64.3	Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking
LEAD	70.7 71.0 70.8	69.3 69.6 69.5	Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking
SCOPE(ChineseBert)	70.1 73.1 71.6	68.6 71.5 70.1	Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity
SCOPE(REALISE)	69.0 75.0 71.9	67.1 72.9 69.9	Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity
SDCL	69.7 70.3 70.0	70.2 67.5 68.8	SDCL: Self-Distillation Contrastive Learning for Chinese Spell Checking
InfoKNN-CSC	72.1 70.6 71.3	71.3 69.8 70.6	Chinese Spelling Check with Nearest Neighbors

sighan15结果:

模型	Detection Level	Correction Level	来源
Hybrid	56.6 69.4 62.3	_ _ 57.1	A Hybrid Approach to Automatic Corpus Generation for Chinese Spelling Check
FASpell	67.6 60.0 63.5	66.6 59.1 62.6	FASPell: A Fast, Adaptable, Simple, Powerful Chinese Spell Checker Based On DAE-Decoder Paradigm
Google BERT	73.7 78.2 75.9	70.9 75.2 73.0	SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check
SpellGCN	74.8 80.7 77.7	72.1 77.7 75.9	SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check
Google BERT	73.0 70.8 71.9	65.9 64.0 64.9	Spelling Error Correction with Soft-Masked BERT
Soft-Masked BERT	73.7 73.2 73.5	66.7 66.2 66.4	Spelling Error Correction with Soft-Masked BERT
HeadFilt	84.5 71.8 77.6	84.2 70.2 76.5	Domain-shift Conditioning using Adaptable Filtering via Hierarchical Embeddings for Robust Chinese Spell Check
Chunk-based CSC	88.11 62.00 72.79	87.33 57.64 69.44	Chunk-based Chinese Spelling Check with Global Optimization
BERT + Pre-trained for CSC	- - 79.8	- - 78.0	Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models
BERT + Adversarial training	- - 80.0	- - 78.2	Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models
BERT-wwm	74.2 78.0 76.1	71.6 75.3 73.4	Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking
REALISE (BERT-wwm)	77.3 81.3 79.3	75.9 79.9 77.8	Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking
BERT_CRS	74.0 80.2 77.2	72.2 77.8 74.8	Global Attention Decoder for Chinese Spelling Error Correction
BERT_CRS_GAD	75.6 80.4 77.9	73.2 77.8 75.4	Global Attention Decoder for Chinese Spelling Error Correction
ERNIE	73.6 79.8 76.6	68.6 74.4 71.4	Correcting Chinese Spelling Errors with Phonetic Pre-training
MLM-phonetics	77.5 83.1 80.2	74.9 80.2 77.5	Correcting Chinese Spelling Errors with Phonetic Pre-training
RoBERTa	74.7 77.3 76.0	72.1 74.5 73.3	Dynamic Connected Networks for Chinese Spelling Check
RoBERTa-DCN	76.6 79.8 78.2	74.2 77.3 75.7	Dynamic Connected Networks for Chinese Spelling Check
RoBERTa-Pretrain-DCN	77.1 80.9 79.0	74.5 78.2 76.3	Dynamic Connected Networks for Chinese Spelling Check
Google BERT	68.4 77.6 72.7	66.0 74.9 70.2	PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction
PLOME	77.4 81.5 79.4	75.3 79.3 77.2	PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction
Google BERT	79.9 84.1 72.9 78.1	83.1 68.0 74.8	PHMOSpell: Phonological and Morphological Knowledge Guided Chinese Spelling Check
PHMOSpell	90.1 72.7 80.5	89.6 69.2 78.1	PHMOSpell: Phonological and Morphological Knowledge Guided Chinese Spelling Check
Soft-Masked BERT SSCL	86.34 72.46 78.79	85.20 65.99 74.38	Self-Supervised Curriculum Learning for Spelling Error Correction
Google BERT(4 layer)	85.2 68.9 76.2	84.8 66.9 74.8	SpellBERT: A Lightweight Pretrained Model for Chinese Spelling Check
SpellBERT (4 layer)	87.5 73.6 80.0	87.1 71.5 78.5	SpellBERT: A Lightweight Pretrained Model for Chinese Spelling Check
ECOPO BERT-wwm	78.2 82.3 80.2	76.6 80.4 78.4	The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking
ECOPO REALISE (BERT-wwm)	77.5 82.6 80.0	76.1 81.2 78.5	The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking
Google BERT	73.7 78.2 75.9	70.9 75.2 73.0	MDCSpell: A Multi-task Detector-Corrector Framework for Chinese Spelling Correction
MDCSpell	80.8 80.6 80.7	78.4 78.2 78.3	MDCSpell: A Multi-task Detector-Corrector Framework for Chinese Spelling Correction
Google BERT	76.0 81.0 78.4	74.7 79.5 77.0	General and Domain Adaptive Chinese Spelling Check with Error Consistent Pretraining
ECSpell	81.1 83.0 81.0	77.5 81.7 79.5	General and Domain Adaptive Chinese Spelling Check with Error Consistent Pretraining
Soft-Masked BERT	73.7 73.2 73.5	66.7 66.2 66.4	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
CL (Soft-Masked BERT)	83.5 74.8 78.9	79.9 72.1 75.8	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
Google BERT	84.1 72.9 78.1	83.1 68.0 74.8	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
CL (BERT)	85.0 74.5 79.4	84.2 72.3 77.8	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
MacBERT	84.3 73.1 78.3	83.3 68.2 75.0	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
CL (MacBERT)	85.8 75.4 80.3	84.7 73.0 78.4	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
uChecker	75.4 72.0 73.7	70.6 67.3 68.9	uChecker: Masked Pretrained Language Models as Unsupervised Chinese Spelling Checkers
Google BERT	74.2 78.0 76.1	71.6 75.3 73.4	Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking
LEAD	79.2 82.8 80.9	77.6 81.2 79.3	Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking
SCOPE(ChineseBert)	81.1 84.3 82.7	79.2 82.3 80.7	Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity
SCOPE(REALISE)	78.7 84.7 81.6	76.8 82.6 79.6	Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity
SDCL	81.2 79.1 80.1	79.3 77.5 78.3	SDCL: Self-Distillation Contrastive Learning for Chinese Spell Checking
InfoKNN-CSC	81.2 81.2 81.2	80.0 80.0 80.0	Chinese Spelling Check with Nearest Neighbors
Google BERT	74.2 78.0 76.1	71.6 75.3 73.4	An Error-Guided Correction Model for Chinese Spelling Error Correction
EGCM	82.7 77.6 80.0	80.6 74.7 77.5	An Error-Guided Correction Model for Chinese Spelling Error Correction
Pre-Tn EGCM	83.4 79.8 81.6	81.4 78.4 79.9	An Error-Guided Correction Model for Chinese Spelling Error Correction

香侬科技ChineseBert用于中文拼写纠错

ChineseBert repo[https://github.com/ShannonAI/ChineseBert]

训练：

下载ChineseBert放出的预训练模型，放置在本地文件夹（chinese_bert_path 参数）
拷贝ChineseBert代码，置于ChineseBert文件夹，并安装ChineseBert所需依赖
运行train.sh

测试：

运行eval.sh

纠正文本：

填入模型路径，运行csc_eval.py 即可

运行结果:

布告栏转眼之间从不起眼的丑小鸭变成了高贵优雅的天鹅！仅管这大改造没有得名，但过程也是很可贵的。
布告栏转眼之间从不起眼的丑小鸭变成了高贵优雅的天鹅！尽管这大改造没有得名，但过程也是很可贵的。
仅->尽
我爱北进天安门
我爱北进天安门
我爱北京天按门
我爱北京天安门
按->安
没过几分钟，救护车来了，发出响亮而清翠的声音
没过几分钟，救护车来了，发出响亮而清翠的声音
我见过一望无际、波澜壮阔的大海；玩赏过水平如镜、诗情画意的西湖；游览过翡翠般的漓江；让我难以忘怀的要数那荷叶飘香、群山坏绕的普者黑。
我见过一望无际、波澜壮阔的大海；玩赏过水平如镜、诗情画意的西湖；游览过翡翠般的漓江；让我难以忘怀的要数那荷叶飘香、群山环绕的普者黑。
坏->环

已经训练好的模型：

链接: https://pan.baidu.com/s/1mi0r2Uvv9rd_bfDONNDVYA?pwd=cvak

指标：

数据集	Detection Level	Correction Level
sighan13	p:0.84,r:0.792,F1:0.815	p:0.823,recall:0.775,F1:0.799
new_data	p:0.491,r:0.398,F1:0.44	p:0.442,recall:0.358,F1:0.396

注意

先用wang2018数据预训练，再用同分布的sighan13微调
使用词表的分词方式容易造成拼音和数字错误修正，因此直接使用一个字作为token，不使用基于词表的分词（csc_train_mlm_tok.py）

Contributors

wangwang110

56 commits

Similar repos

Macropodus/bert4csc_v1

Model

Macropodus/macbert4csc_v1

Model

destwang/CTCResources

269

Macropodus/macbert4csc_v2

Model

Macropodus/macbert4mdcspell_v1

Model

Macropodus/macbert4mdcspell_v3

Model

Macropodus/macbert4mdcspell_v2

Model

nghuyong/Chinese-text-correction-papers

text correction papers

316

Languages

Python

98.2%

Shell

1.8%

wangwang110/CSC

ChineseBert用于中文拼写纠错

stars

commits

Python

primary language

Mar 14, 2023

updated

Browse cluster: Chinese Spelling Error Correction →

README

CSC

中文拼写纠错

本项目只关注Chinese Spelling Check (CSC)，不考虑多字少字的语法纠错。

关于语法纠错可参考 https://github.com/HillZhang1999/MuCGEC

常用数据来源:

SIGHAN Bake-off 2013: http://ir.itc.ntnu.edu.tw/lre/sighan7csc.html

SIGHAN Bake-off 2014: http://ir.itc.ntnu.edu.tw/lre/clp14csc.html

SIGHAN Bake-off 2015: http://ir.itc.ntnu.edu.tw/lre/sighan8csc.html

Wang271K: https://github.com/wdimmy/Automatic-Corpus-Generation/tree/master/corpus

sighan13，sighan14，sighan15 包含对应的训练集和测试集，Wang271K是论文利用OCR以及语音识别的方法生成的数据构，仅仅用来训练

评价指标

使用句子级纠正F1值。

关于句子级别纠正P值，有两种计算方式

分母不考虑修改了原句但是与正确句子不同的情况
分母考虑修改了原句但是与正确句子不同的情况

使用第一种计算方式得到的结果偏高，这里采用第2种

代码

该指标更为严格（所有修正过的句子都算作P值的分母），论文大都使用该评价指标

榜单

各论文指标（不同的论文使用bert取得的结果不一样,可能是使用的预训练数据不一样或者超参数的设置不同）

sighan13结果:

模型	Detection Level	Correction Level	来源
Hybrid	54.0 69.3 60.7	_ _ 52.1	A Hybrid Approach to Automatic Corpus Generation for Chinese Spelling Check
FASpell	76.2 63.2 69.1	73.1 60.5 66.2	FASPell: A Fast, Adaptable, Simple, Powerful Chinese Spell Checker Based On DAE-Decoder Paradigm
Google BERT	79.0 72.8 75.8	77.7 71.6 74.6	SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check
SpellGCN	80.1 74.4 77.2	78.3 72.7 75.4	SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check
HeadFilt	100.0 74.9 85.7	100.0 74.1 85.1	Domain-shift Conditioning using Adaptable Filtering via Hierarchical Embeddings for Robust Chinese Spell Check
Chunk-based CSC	61.19 75.67 67.66	74.34 67.20 70.59	Chunk-based Chinese Spelling Check with Global Optimization
BERT + Pre-trained for CSC	- - 84.9	- - 84.4	Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models
BERT + Adversarial training	- - 84.0	- - 83.5	Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models
SpellGCN+	85.7 78.8 82.1	84.6 77.8 81.0	Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking
BERT-wwm	85.0 77.0 80.8	83.0 75.2 78.9	Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking
REALISE (BERT-wwm )	88.6 82.5 85.4	887.2 81.2 84.1	Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking
BERT_CRS	84.8 79.5 82.1	83.9 78.7 81.2	Global Attention Decoder for Chinese Spelling Error Correction
BERT_CRS_GAD	85.7 79.5 82.5	84.9 78.7 81.6	Global Attention Decoder for Chinese Spelling Error Correction
ERNIE	76.6 71.9 74.2	73.0 68.5 70.6	Correcting Chinese Spelling Errors with Phonetic Pre-training
MLM-phonetics	82.0 78.3 80.1	79.5 77.0 78.2	Correcting Chinese Spelling Errors with Phonetic Pre-training
RoBERTa	85.4 77.7 81.3	83.9 76.4 79.9	Dynamic Connected Networks for Chinese Spelling Check
RoBERTa-DCN	86.2 78.4 82.1	84.6 76.9 80.5	Dynamic Connected Networks for Chinese Spelling Check
RoBERTa-Pretrain-DCN	86.8 79.6 83.0	84.7 77.7 81.0	Dynamic Connected Networks for Chinese Spelling Check
Google BERT	98.7 70.6 82.3	98.6 67.8 80.4	PHMOSpell: Phonological and Morphological Knowledge Guided Chinese Spelling Check
PHMOSpell	99.5 76.8 86.7	99.5 75.1 85.6	PHMOSpell: Phonological and Morphological Knowledge Guided Chinese Spelling Check
ECOPO BERT-wwm	87.2 81.7 84.4	86.1 80.6 83.3	The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking
ECOPO REALISE (BERT-wwm )	89.3 83.2 86.2	88.5 82.0 85.1	The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking
Google BERT	79.0 72.8 75.8	77.7 71.6 74.6	MDCSpell: A Multi-task Detector-Corrector Framework for Chinese Spelling Correction
MDCSpell	89.1 78.3 83.4	87.5 76.8 81.8	MDCSpell: A Multi-task Detector-Corrector Framework for Chinese Spelling Correction
Soft-Masked BERT	81.1 75.7 78.3	75.1 70.1 72.5	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
CL (Soft-Masked BERT)	84.7 77.0 80.7	80.9 74.5 77.6	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
Google BERT	98.7 70.6 82.3	98.6 67.8 80.4	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
CL (BERT)	99.1 74.8 85.3	99.1 73.2 84.2	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
MacBERT	98.7 70.8 82.5	98.6 67.9 80.4	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
CL (MacBERT)	99.3 75.7 85.9	99.2 73.8 84.6	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
uChecker	75.4 73.4 74.4	72.6 70.8 71.7	uChecker: Masked Pretrained Language Models as Unsupervised Chinese Spelling Checkers
Google BERT	85.0 77.0 80.8	83.0 75.2 78.9	Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking
LEAD	88.3 83.4 85.8	87.2 82.4 84.7	Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking
SCOPE(ChineseBert)	87.4 83.4 85.4	86.3 82.4 84.3	Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity
SCOPE(REALISE)	87.5 83.2 85.3	86.4 82.3 84.3	Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity
SDCL	88.9 81.8 85.2	88.0 81.0 84.3	SDCL: Self-Distillation Contrastive Learning for Chinese Spell Checking
InfoKNN-CSC	89.7 82.8 86.1	82.1 88.7 81.9 85.2	Chinese Spelling Check with Nearest Neighbors

sighan14结果:

模型	Detection Level	Correction Level	来源
Hybrid	51.9 66.2 58.2	_ _ 56.1	A Hybrid Approach to Automatic Corpus Generation for Chinese Spelling Check
FASpell	61.0 53.5 57.0	59.4 52.0 55.4	FASPell: A Fast, Adaptable, Simple, Powerful Chinese Spell Checker Based On DAE-Decoder Paradigm
Google BERT	65.6 68.1 66.8	63.1 65.5 64.3	SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check
SpellGCN	65.1 69.5 67.2	63.1 67.2 65.3	SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check
HeadFilt	82.5 61.6 70.5	82.1 60.2 69.4	Domain-shift Conditioning using Adaptable Filtering via Hierarchical Embeddings for Robust Chinese Spell Check
Chunk-based CSC	78.65 54.80 64.59	77.43 51.04 61.52	Chunk-based Chinese Spelling Check with Global Optimization
BERT + Pre-trained for CSC	- - 70.4	- - 68.6	Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models
BERT + Adversarial training	- - 68.4	- - 66.8	Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models
BERT-wwm	64.5 68.6 66.5	62.4 66.3 64.3	Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking
REALISE (BERT-wwm )	67.8 71.5 69.6	66.3 70.0 68.1	Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking
BERT_CRS	65.4 72.7 68.9	63.4 70.4 66.7	Global Attention Decoder for Chinese Spelling Error Correction
BERT_CRS_GAD	66.6 71.8 69.1	65.0 70.1 67.5	Global Attention Decoder for Chinese Spelling Error Correction
ERNIE	63.5 69.3 66.3	60.1 65.6 62.8	Correcting Chinese Spelling Errors with Phonetic Pre-training
MLM-phonetics	66.2 73.8 69.8	64.2 73.8 68.7	Correcting Chinese Spelling Errors with Phonetic Pre-training
RoBERTa	64.2 68.4 66.2	62.7 66.7 64.6	Dynamic Connected Networks for Chinese Spelling Check
RoBERTa-DCN	67.6 68.6 68.0	64.9 65.9 65.4	Dynamic Connected Networks for Chinese Spelling Check
RoBERTa-Pretrain-DCN	67.4 70.4 68.9	65.8 68.7 67.2	Dynamic Connected Networks for Chinese Spelling Check
Google BERT	78.6 60.7 68.5	77.8 57.6 66.2	PHMOSpell: Phonological and Morphological Knowledge Guided Chinese Spelling Check
PHMOSpell	85.3 67.6 75.5	84.7 64.3 73.1	PHMOSpell: Phonological and Morphological Knowledge Guided Chinese Spelling Check
Google BERT(4 layer)	82.6 59.0 68.8	82.4 58.0 68.1	SpellBERT: A Lightweight Pretrained Model for Chinese Spelling Check
SpellBERT (4 layer)	83.1 62.0 71.0	82.9 61.2 70.4	SpellBERT: A Lightweight Pretrained Model for Chinese Spelling Check
ECOPO BERT-wwm	65.8 69.0 67.4	63.7 66.9 65.3	The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking
ECOPO REALISE (BERT-wwm )	68.8 72.1 70.4	67.5 71.0 69.2	The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking
Google BERT	65.6 68.1 66.8	63.1 65.5 64.3	MDCSpell: A Multi-task Detector-Corrector Framework for Chinese Spelling Correction
MDCSpell	70.2 68.8 69.5	69.0 67.7 68.3	MDCSpell: A Multi-task Detector-Corrector Framework for Chinese Spelling Correction
Soft-Masked BERT	65.2 70.4 67.7	63.7 68.7 66.1	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
CL (Soft-Masked BERT)	68.4 70.9 69.6	67.8 69.1 68.4	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
Google BERT	78.6 60.7 68.5	77.8 57.6 66.2	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
CL (BERT)	79.2 61.6 69.3	78.5 60.8 68.5	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
MacBERT	78.8 61.0 68.8	78.0 58.0 66.5	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
CL (MacBERT)	79.7 62.4 70.0	79.0 61.4 69.1	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
uChecker	61.7 61.5 61.6	57.6 57.5 57.6	uChecker: Masked Pretrained Language Models as Unsupervised Chinese Spelling Checkers
Google BERT	64.5 68.6 66.5	62.4 66.3 64.3	Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking
LEAD	70.7 71.0 70.8	69.3 69.6 69.5	Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking
SCOPE(ChineseBert)	70.1 73.1 71.6	68.6 71.5 70.1	Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity
SCOPE(REALISE)	69.0 75.0 71.9	67.1 72.9 69.9	Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity
SDCL	69.7 70.3 70.0	70.2 67.5 68.8	SDCL: Self-Distillation Contrastive Learning for Chinese Spell Checking
InfoKNN-CSC	72.1 70.6 71.3	71.3 69.8 70.6	Chinese Spelling Check with Nearest Neighbors

sighan15结果:

模型	Detection Level	Correction Level	来源
Hybrid	56.6 69.4 62.3	_ _ 57.1	A Hybrid Approach to Automatic Corpus Generation for Chinese Spelling Check
FASpell	67.6 60.0 63.5	66.6 59.1 62.6	FASPell: A Fast, Adaptable, Simple, Powerful Chinese Spell Checker Based On DAE-Decoder Paradigm
Google BERT	73.7 78.2 75.9	70.9 75.2 73.0	SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check
SpellGCN	74.8 80.7 77.7	72.1 77.7 75.9	SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check
Google BERT	73.0 70.8 71.9	65.9 64.0 64.9	Spelling Error Correction with Soft-Masked BERT
Soft-Masked BERT	73.7 73.2 73.5	66.7 66.2 66.4	Spelling Error Correction with Soft-Masked BERT
HeadFilt	84.5 71.8 77.6	84.2 70.2 76.5	Domain-shift Conditioning using Adaptable Filtering via Hierarchical Embeddings for Robust Chinese Spell Check
Chunk-based CSC	88.11 62.00 72.79	87.33 57.64 69.44	Chunk-based Chinese Spelling Check with Global Optimization
BERT + Pre-trained for CSC	- - 79.8	- - 78.0	Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models
BERT + Adversarial training	- - 80.0	- - 78.2	Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models
BERT-wwm	74.2 78.0 76.1	71.6 75.3 73.4	Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking
REALISE (BERT-wwm)	77.3 81.3 79.3	75.9 79.9 77.8	Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking
BERT_CRS	74.0 80.2 77.2	72.2 77.8 74.8	Global Attention Decoder for Chinese Spelling Error Correction
BERT_CRS_GAD	75.6 80.4 77.9	73.2 77.8 75.4	Global Attention Decoder for Chinese Spelling Error Correction
ERNIE	73.6 79.8 76.6	68.6 74.4 71.4	Correcting Chinese Spelling Errors with Phonetic Pre-training
MLM-phonetics	77.5 83.1 80.2	74.9 80.2 77.5	Correcting Chinese Spelling Errors with Phonetic Pre-training
RoBERTa	74.7 77.3 76.0	72.1 74.5 73.3	Dynamic Connected Networks for Chinese Spelling Check
RoBERTa-DCN	76.6 79.8 78.2	74.2 77.3 75.7	Dynamic Connected Networks for Chinese Spelling Check
RoBERTa-Pretrain-DCN	77.1 80.9 79.0	74.5 78.2 76.3	Dynamic Connected Networks for Chinese Spelling Check
Google BERT	68.4 77.6 72.7	66.0 74.9 70.2	PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction
PLOME	77.4 81.5 79.4	75.3 79.3 77.2	PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction
Google BERT	79.9 84.1 72.9 78.1	83.1 68.0 74.8	PHMOSpell: Phonological and Morphological Knowledge Guided Chinese Spelling Check
PHMOSpell	90.1 72.7 80.5	89.6 69.2 78.1	PHMOSpell: Phonological and Morphological Knowledge Guided Chinese Spelling Check
Soft-Masked BERT SSCL	86.34 72.46 78.79	85.20 65.99 74.38	Self-Supervised Curriculum Learning for Spelling Error Correction
Google BERT(4 layer)	85.2 68.9 76.2	84.8 66.9 74.8	SpellBERT: A Lightweight Pretrained Model for Chinese Spelling Check
SpellBERT (4 layer)	87.5 73.6 80.0	87.1 71.5 78.5	SpellBERT: A Lightweight Pretrained Model for Chinese Spelling Check
ECOPO BERT-wwm	78.2 82.3 80.2	76.6 80.4 78.4	The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking
ECOPO REALISE (BERT-wwm)	77.5 82.6 80.0	76.1 81.2 78.5	The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking
Google BERT	73.7 78.2 75.9	70.9 75.2 73.0	MDCSpell: A Multi-task Detector-Corrector Framework for Chinese Spelling Correction
MDCSpell	80.8 80.6 80.7	78.4 78.2 78.3	MDCSpell: A Multi-task Detector-Corrector Framework for Chinese Spelling Correction
Google BERT	76.0 81.0 78.4	74.7 79.5 77.0	General and Domain Adaptive Chinese Spelling Check with Error Consistent Pretraining
ECSpell	81.1 83.0 81.0	77.5 81.7 79.5	General and Domain Adaptive Chinese Spelling Check with Error Consistent Pretraining
Soft-Masked BERT	73.7 73.2 73.5	66.7 66.2 66.4	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
CL (Soft-Masked BERT)	83.5 74.8 78.9	79.9 72.1 75.8	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
Google BERT	84.1 72.9 78.1	83.1 68.0 74.8	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
CL (BERT)	85.0 74.5 79.4	84.2 72.3 77.8	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
MacBERT	84.3 73.1 78.3	83.3 68.2 75.0	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
CL (MacBERT)	85.8 75.4 80.3	84.7 73.0 78.4	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
uChecker	75.4 72.0 73.7	70.6 67.3 68.9	uChecker: Masked Pretrained Language Models as Unsupervised Chinese Spelling Checkers
Google BERT	74.2 78.0 76.1	71.6 75.3 73.4	Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking
LEAD	79.2 82.8 80.9	77.6 81.2 79.3	Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking
SCOPE(ChineseBert)	81.1 84.3 82.7	79.2 82.3 80.7	Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity
SCOPE(REALISE)	78.7 84.7 81.6	76.8 82.6 79.6	Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity
SDCL	81.2 79.1 80.1	79.3 77.5 78.3	SDCL: Self-Distillation Contrastive Learning for Chinese Spell Checking
InfoKNN-CSC	81.2 81.2 81.2	80.0 80.0 80.0	Chinese Spelling Check with Nearest Neighbors
Google BERT	74.2 78.0 76.1	71.6 75.3 73.4	An Error-Guided Correction Model for Chinese Spelling Error Correction
EGCM	82.7 77.6 80.0	80.6 74.7 77.5	An Error-Guided Correction Model for Chinese Spelling Error Correction
Pre-Tn EGCM	83.4 79.8 81.6	81.4 78.4 79.9	An Error-Guided Correction Model for Chinese Spelling Error Correction

香侬科技ChineseBert用于中文拼写纠错

ChineseBert repo[https://github.com/ShannonAI/ChineseBert]

训练：

下载ChineseBert放出的预训练模型，放置在本地文件夹（chinese_bert_path 参数）
拷贝ChineseBert代码，置于ChineseBert文件夹，并安装ChineseBert所需依赖
运行train.sh

测试：

运行eval.sh

纠正文本：

填入模型路径，运行csc_eval.py 即可

运行结果:

布告栏转眼之间从不起眼的丑小鸭变成了高贵优雅的天鹅！仅管这大改造没有得名，但过程也是很可贵的。
布告栏转眼之间从不起眼的丑小鸭变成了高贵优雅的天鹅！尽管这大改造没有得名，但过程也是很可贵的。
仅->尽
我爱北进天安门
我爱北进天安门
我爱北京天按门
我爱北京天安门
按->安
没过几分钟，救护车来了，发出响亮而清翠的声音
没过几分钟，救护车来了，发出响亮而清翠的声音
我见过一望无际、波澜壮阔的大海；玩赏过水平如镜、诗情画意的西湖；游览过翡翠般的漓江；让我难以忘怀的要数那荷叶飘香、群山坏绕的普者黑。
我见过一望无际、波澜壮阔的大海；玩赏过水平如镜、诗情画意的西湖；游览过翡翠般的漓江；让我难以忘怀的要数那荷叶飘香、群山环绕的普者黑。
坏->环