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ReaLiSe

ReaLiSe is a multi-modal Chinese spell checking model.

This the office code for the paper Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking.

The paper has been accepted in ACL Findings 2021.

Environment

• Python: 3.6
• Cuda: 10.0
• Packages:

  pip install --editable . 
  pip install -r requirements.txt
  

Data

Raw Data

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

Data Processing

The code and cleaned data are in the data_process directory.

You can also directly download the processed data from this and put them in the data directory. The data directory would look like this:

data
|- trainall.times2.pkl
|- test.sighan15.pkl
|- test.sighan15.lbl.tsv
|- test.sighan14.pkl
|- test.sighan14.lbl.tsv
|- test.sighan13.pkl
|- test.sighan13.lbl.tsv

Pretrain

• BERT: chinese-roberta-wwm-ext

  Huggingface hfl/chinese-roberta-wwm-ext: https://huggingface.co/hfl/chinese-roberta-wwm-ext
  Local: /data/dobby_ceph_ir/neutrali/pretrained_models/roberta-base-ch-for-csc/

• Phonetic Encoder: pretrain_pho.sh

• Graphic Encoder: pretrain_res.sh

• Merge: merge.py

You can also directly download the pretrained and merged BERT, Phonetic Encoder, and Graphic Encoder from this, and put them in the pretrained directory:

pretrained
|- pytorch_model.bin
|- vocab.txt
|- config.json

Train

After preparing the data and pretrained model, you can train ReaLiSe by executing the train.sh script. Note that you should set up the PRETRAINED_DIR, DATE_DIR, and OUTPUT_DIR in it.

sh train.sh

Test

Test ReaLiSe using the test.sh script. You should set up the DATE_DIR, CKPT_DIR, and OUTPUT_DIR in it. CKPT_DIR is the OUTPUT_DIR of the training process.

sh test.sh

Well-trained Model

You can also download well-trained model from this direct using. The performance scores of RealiSe and some baseline models on the SIGHAN13, SIGHAN14, SIGHAN15 test set are here:

Methods

• FASpell: FASPell: A Fast, Adaptable, Simple, Powerful Chinese Spell Checker Based On DAE-Decoder Paradigm
• Soft-Masked BERT: Spelling Error Correction with Soft-Masked BERT
• SpellGCN: SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check
• BERT: Our implementation

Metrics

• "D" means "Detection Level", "C" means "Correction Level".
• "A", "P", "R", "F" means "Accuracy", "Precision", "Recall", and "F1" respectively.

SIGHAN15

SIGHAN14

SIGHAN13

Citation

@misc{xu2021read,
      title={Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking}, 
      author={Heng-Da Xu and Zhongli Li and Qingyu Zhou and Chao Li and Zizhen Wang and Yunbo Cao and Heyan Huang and Xian-Ling Mao},
      year={2021},
      eprint={2105.12306},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}