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Dynamic Connected Networks for Chinese Spelling Check

This repository provides training code of DCN models for Chinese Spelling Check (CSC).

The paper has been accepted in Findings of ACL 2021.

Installation

Our code is based on transformers 3.0.

The following command installs all necessary packages:

pip install -r requirements.txt

We test our code using Python 3.6.

Datasets

The preprocessed training dataset can be downloaded from here(password:hfiw).

Train Model

To train the DCN model, download the RoBERTa-wwm-ext and copy the model to chinese_roberta_wwm_ext_pytorch, then run:

sh train.sh

Experimental Result

The sentence-level experimental results on SIGHAN15 for the default config are as follows:

model d-p d-r d-f c-p c-r c-f
DCN 76.84 79.64 78.21 74.74 77.45 76.07

Citation

@inproceedings{wang-etal-2021-dynamic,
    title = "Dynamic Connected Networks for {C}hinese Spelling Check",
    author = "Wang, Baoxin  and
      Che, Wanxiang  and
      Wu, Dayong  and
      Wang, Shijin  and
      Hu, Guoping  and
      Liu, Ting",
    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-acl.216",
    doi = "10.18653/v1/2021.findings-acl.216",
    pages = "2437--2446",
}

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