Skip to content

bcmi220/ccharpar

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

1 Commit
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

CCharPar

This is the main code of paper "Neural Character-Level Syntactic Parsing for Chinese", which is published in JAIR 2022.

Contents

Requirements

  • Python 3.6 or higher.
  • Cython 0.25.2 or any compatible version.
  • PyTorch 1.0.0. This code has not been tested with PyTorch 1.6.0, but it should work.
  • EVALB. Before starting, run make inside the EVALB/ directory to compile an evalb executable. This will be called from Python for evaluation.
  • transformers PyTorch 1.0.0+ or any compatible version (only required when using BERT, XLNet, etc.)

Training

CUDA_VISIBLE_DEVICES=1 python src_chardep/main.py train \
 --model-path-base models/1014_scdt8layer_notag \
 --epochs 200 \
 --joint-syn-dep \
 --joint-syn-const \
 --pos-layer 4 \
 --use-words \
 --const-lada 0.7 \
 --dataset char \
 --num-layers 8 \
 --num-heads 8 \
 --learning-rate 0.0005 \
 --batch-size 200 \
 --eval-batch-size 30 \
 --subbatch-max-tokens 1000 \
 --embedding-path data/csskip.gz \
 --model-name cl/1014_scdt8layer_notag \
 --embedding-type sskip \
 --checks-per-epoch 1

Citation

If you use this software for research, please cite our paper as follows:

@article{li2022neural,
  title={Neural Character-Level Syntactic Parsing for Chinese},
  author={Li, Zuchao and Zhou, Junru and Zhao, Hai and Zhang, Zhisong and Li, Haonan and Ju, Yuqi},
  journal={Journal of Artificial Intelligence Research},
  volume={73},
  pages={461--509},
  year={2022}
}

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published