Code for DataAugmentationCGED

This repo provides the Code for DataAugmentationCGED Paper in ACL 2022.

Pipeline

• 1：Prepare the training data for the error generation model.
  • python3 error_generation/train_data_process.py
• 2：Train the model and predict the errors
  • sh error_generation/train.sh
• 3： Filter non-error generated spans by span-level perplexity
  • sh noise_filter/start.sh
• 4: auto label the generated span by editing method
  • sh auto_label/run.sh
• 5: construct the final training sample
  • sh sample_construction/run.sh
• 6: train the detection model with the augmented dataset
  • sh error_detection/train.sh

Dataset

• We don't have the copyright of the dataset. Please contact with the host of the CGED shared task
• The sample data is in ./data/train_data_process.txt.sample:

Requirements

• transformers2.0.0
• RoBERTa-wwm-ext, Chinese. From repo