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Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models

This is the official code for paper titled "Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models".

We propose a method, which continually identifies the weak spots of a model to generate more valuable training instances, and apply a task-specific pre-training strategy to enhance the model. Experimental results show that such an adversarial training method combined with the pre-training strategy can improve both the generalization and robustness of multiple CSC models across three different datasets, achieving state-of-the-art performance for CSC task.

Requirements

For BERT and Soft-Masked BERT:

python==3.7
pytorch==1.4.0
transformers==3.4.0

For SpellGCN, we borrow some codes from SpellGCN, thus our requirements are the same with their.

Tensorflow==1.13.1
python==2.7
"BERT-Base, Chinese" from google-research

How to run?

1. Prepare the datasets:

For pre-train:
1. Wiki: Download the latest zhwiki and process the dump with gensim.
2. Weibo: Download the Weibo datasets.
3. Pre-process the corpus to remove noise, such as splitting paragraphs into sentences, filtering out the inappropriate sentences (too long or too short) and so on.
For train:
1. Download the additional 270K data samples from here.
2. Extract the training samples from the file "train.sgml".
Note: The data samples mentioned above are absent here due to the lack of permission.

2. Run the models:

For BERT and Soft-Masked BERT:

Set up an virtual environment for BERT and Soft-Masked BERT(python==3.7,torch==1.4.0,transformers==3.4.0) using Anaconda
```
conda create -n bert python=3.7.9
conda activate bert
pip install torch==1.4.0
pip install transformers==3.4.0
```
Go to the directory "scripts", set up your private parameters(like the path of initial model and data)
```
cd scripts
vim run.sh
```
bash run.sh
```
bash run.sh
```

For SpellGCN:

Set up an virtual environment for SpellGCN (python==2.7, Tensorflow==1.13.1) using Anaconda

conda create -n spellgcn python=2.7.1
source activate spellgcn
pip install tensorflow==1.13.1

Go to the directory "scripts", set up your private parameters (like the path of BERT and initial model)
```
cd scripts
vim run.sh
```
bash run.sh
```
bash run.sh
```

3. Or you can download the models you need and initialize your models from them.

Baidu Wangpan:
- 链接：https://pan.baidu.com/s/1O9mLjWSiXzxcPBy0fU-_BQ 提取码：y25e

Contact

chongli17@fudan.edu.cn and cenyuanzhang17@fudan.edu.cn

How to cite our paper?

@inproceedings{li-etal-2021-2Ways,
  author    = {Chong Li and
               Cenyuan Zhang and
               Xiaoqing Zheng and
               Xuanjing Huang},
  title="Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models",
  booktitle="Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing",
  publisher = "Association for Computational Linguistics",
  year="2021"
}