https://github.com/kanyun-inc/fairseq-gec

Source code for paper: Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data
https://github.com/kanyun-inc/fairseq-gec

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Source code for paper: Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data

• Host: GitHub
• URL: https://github.com/kanyun-inc/fairseq-gec
• Owner: kanyun-inc
• License: other
• Created: 2019-05-04T07:35:58.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2020-06-03T04:13:31.000Z (over 4 years ago)
• Last Synced: 2025-01-16T23:02:10.261Z (26 days ago)
• Topics: grammar, nlp
• Language: Python
• Homepage:
• Size: 2.72 MB
• Stars: 246
• Watchers: 7
• Forks: 67
• Open Issues: 18
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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README

        
# Introduction

Source code for the paper: 

**Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data**

Authors: Wei Zhao, Liang Wang, Kewei Shen, Ruoyu Jia, Jingming Liu

Arxiv url: https://arxiv.org/abs/1903.00138

Comments: Accepted by NAACL 2019 (oral)

![](arch.jpg)

## Dependecies

- PyTorch version >= 1.0.0

- Python version >= 3.6

## Downloads

- Download CoNLL-2014 evaluation scripts

```

cd gec_scripts/

sh download.sh

```

- Download **pre-processed data** & **pre-trained models**

  

  pre-trained model: (Google Drive/Baidu Pan) 

    - url1: https://drive.google.com/file/d/1zewifHUUwvqc2F-MfDRsZFio6PlSzx2c/view?usp=sharing

    - url2: https://pan.baidu.com/s/1hCwQeNFjng_0_NiViJq6fg (code: mxrf)

    

  pre-processed data: (Google Drive)(train/valid/test)

    - url: https://drive.google.com/open?id=17s-TZiM6ilQ-SHklxTUun2Jdgg8B9zS3  

## Train with the pre-trained model

```

cd fairseq-gec

pip install --editable

sh train.sh \${device_id} \${experiment_name}

```

## Train without the pre-trained model

Modify train.sh to train without the pre-trained model

- delete parameter "--pretrained-model" 

- change the value of "--max-epoch" to 15 (more epochs are needed without pre-trained parameters) 

## Evaluate on the CoNLL-2014 test dataset

```

sh g.sh \${device_id} \${experiment_name}

```

## Get pre-trained models from scratch

We have public our pre-trained models as mentioned in the downloads part. We list the steps here, in case someone want to get the pre-trained models from scratch. 

```

1. # prepare target sentences using one billion benchmark dataset

2. sh noise.sh # generate the noised source sentences 

3. sh preprocess_noise_data.sh # preprocess data

4. sh pretrain.sh 0,1 _pretrain # pretrain 

```

## Acknowledgments

Our code was modified from [fairseq](https://github.com/pytorch/fairseq) codebase. We use the same license as fairseq(-py).

## Citation

Please cite as:

```

@article{zhao2019improving,

  title={Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data},

    author={Zhao, Wei and Wang, Liang and Shen, Kewei and Jia, Ruoyu and Liu, Jingming},

      journal={arXiv preprint arXiv:1903.00138},

        year={2019}

}

```