TwASP

This is the implementation of Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-way Attentions of Auto-analyzed Knowledge 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-joint,
    title = "Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-way Attentions of Auto-analyzed Knowledge",
    author = "Tian, Yuanhe and Song, Yan and Ao, Xiang and Xia, Fei and Quan, Xiaojun 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 = "8286--8296",
}

Requirements

Our code works with the following environment.

• python=3.6
• pytorch=1.1

To run Stanford CoreNLP Toolkit, you need

• Java 8

To run Berkeley Neural Parser, you need

• tensorfolw==1.13.1
• benepar[cpu]
• cython

Note that Berkeley Neural Parser does not support TensorFlow 2.0.

You can refer to their websites for more information.

Downloading BERT, ZEN and TwASP

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.

For TwASP, you can download the models we trained in our experiments from here.

Run on Sample Data

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

Datasets

We use CTB5, CTB6, CTB7, CTB9, and Universal Dependencies 2.4 (UD) in our paper.

To obtain and pre-process the data, you can go to data_preprocessing directory and run getdata.sh. This script will download and process the official data from UD. For CTB5 (LDC05T01), CTB6 (LDC07T36), CTB7 (LDC10T07), and CTB9 (LDC2016T13), you need to obtain the official data yourself, and then put the raw data folder under the data_preprocessing directory.

The script will also download the Stanford CoreNLP Toolkit v3.9.2 (SCT) and Berkeley Neural Parser (BNP) to obtain the auto-analyzed syntactic knowledge. You can refer to their website for more information.

All processed data will appear in data directory organized by the datasets, where each of them contains the files with the same file names under the sample_data directory.

Training and Testing

You can find the command lines to train and test model on a specific dataset with the part-of-speech (POS) knowledge from Stanford CoreNLP Toolkit v3.9.2 (SCT) 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_attention: use two-way attention
• --source: the toolkit to be use (stanford or berkeley)
• --feature_flag: use pos, chunk, or dep knowledge
• --model_name: the name of model to save