WeiDC

This is the implementation of Weighted self Distillation for Chinese word segmentation at Findings of the Association for Computational Linguistics: ACL 2022.

Requirements

Our code works with the following environment.

• python=3.6.13
• pytorch=1.7

Downloading BERT or RoBERTa

In our paper, we use BERT (paper), please download pre-trained BERT-Base Chinese from Google or from HuggingFace. You can also choose RoBERTa from roberta_zh.

Datasets

We use SIGHAN2005 in our paper.

To obtain pre-processed data, please follow Improving Chinese Word Segmentation with Wordhood Memory Networks.

Furthermore, we conduct some NER tasks (including WEIBO, RESUME, and MSRA).

The WeiDC

For AttenCD, you can download our codes to train models.

You can find the command lines to train and test models on a specific dataset in run.sh.

Training

Here are some important parameters:

• --do_train: training mode.
• --num_wei(num_atten): weight class.
• --ratio: control the amount of training data.
• --data_name: the name of the data to be trained.
• --dict_path: the dictionary path.
• --bert_model: the directory of pre-trained BERT or RoBERTa model.
• --model_name: the name of model to save.

Testing

Here are some important parameters:

• --do_test: testing mode.
• --test_model: the pre-trained AttenCD model.
• --data_name: the name of the data to be tested.
• --dict_path: the dictionary path.

Predicting

note: Only the prediction method of CWS task is implemented.

Here are some important parameters:

• --do_predict: predicting mode.
• --input_file: the file to be predicted.
• --output_file: the path of the output file.
• --test_model: the pre-trained AttenCD model.

Citation

If you use or extend our work, please cite our paper at ACL2022. bibtex

@inproceedings{DBLP:conf/acl/HeC0Z22,
  author    = {Rian He and
               Shubin Cai and
               Zhong Ming and
               Jialei Zhang},
  editor    = {Smaranda Muresan and
               Preslav Nakov and
               Aline Villavicencio},
  title     = {Weighted self Distillation for Chinese word segmentation},
  booktitle = {Findings of the Association for Computational Linguistics: {ACL} 2022,
               Dublin, Ireland, May 22-27, 2022},
  pages     = {1757--1770},
  publisher = {Association for Computational Linguistics},
  year      = {2022},
  url       = {https://aclanthology.org/2022.findings-acl.139},
  timestamp = {Thu, 19 May 2022 16:52:59 +0200},
  biburl    = {https://dblp.org/rec/conf/acl/HeC0Z22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

others

You can leave comments in the Issues section, if you have any questions about our methods.