WMSeg

This is the implementation of Improving Chinese WordSegmentation with Wordhood Memory Networks at ACL2020.

We will keep updating this repository these days.

Citation

If you use or extend our work, please cite our paper at ACL2020.

@inproceedings{tian-etal-2020-improving,
    title = "Improving Chinese Word Segmentation with Wordhood Memory Networks",
    author = "Tian, Yuanhe and Song, Yan and Xia, Fei and Zhang, Tong and Wang, Yonggang",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    pages = "8274--8285",
}

Requirements

Our code works with the following environment.

• python=3.6
• pytorch=1.1

Downloading BERT and ZEN

In our paper, we use BERT (paper) and ZEN (paper) as the encoder.

For BERT, please download pre-trained BERT-Base Chinese from Google or from HuggingFace. If you download it from Google, you need to convert the model from TensorFlow version to PyTorch version.

For ZEN, you can download the pre-trained model form here.

Run on Sample Data

Run run_sample.sh to train a model on the small sample data under the sample_data directory.

Datasets

We use SIGHAN2005 and CTB6 in our paper.

To obtain and pre-process the data, please go to data_preprocessing directory and run getdata.sh. This script will download and process the official data from SIGHAN2005. For CTB6, you need to obtain the official data first, and then put the LDC07T36 folder under the data_preprocessing directory.

All processed data will appear in data directory.

Training and Testing

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

Here are some important parameters:

• --do_train: train the model
• --do_test: test the model
• --use_bert: use BERT as encoder
• --use_zen: use ZEN as encoder
• --bert_model: the directory of pre-trained BERT/ZEN model
• --use_memory: use memory
• --decoder: use crf or softmax as the decoder
• --ngram_flag: use av, dlg, or pmi to construct the lexicon N
• --model_name: the name of model to save