LADAN

The source code of article "Distinguish Confusing Law Articles for Legal Judgment Prediction", ACL 2020

Data_processing

When you get the CAIL datasets, run 'data_and_config/data/tongji3.py' to get '_{}cs.json' first. Then choose the corresponding code among 'data_processed/data_pickle.py', 'data_processed_big/data_pickle_big.py', and 'data/make_Legal_basis_data.py' to generate the data structure according to model you want to run.

You can get the word embeddin file "cail_thulac.npy" at the following address: https://drive.google.com/file/d/1_j1yYuG1VSblMuMCZrqrL0AtxadFUaXC/view?usp=drivesdk+ the dataset file "CAIL2018.zip" at the address: https://drive.google.com/file/d/12QOsAumyzmdsqgqhqcNOhOaGcp--ECnm/view?usp=sharing https://drive.google.com/file/d/1JsP6co2GiCodv4oqyjaa6Q8TY5FWOQDM/view?usp=sharing and https://drive.google.com/file/d/1VX8YhqF6ZX2tVLeOJV4VxdnNkWEDrbyS/view?usp=sharing

Usage

The "training_code" folder contains the training code of our LADAN. And the "data_and_config" folder contains the deta preprocessing codes and processed datasets. Here are details of some important folders in the "data_and_config" folder:

• config: Records the experimental parameter setting.
• Model: Contains some basic models we use. others: the data preprocessing codes.

For training, you should move all folders in the "data_and_config" folder into "training_code" folder first, i.e.,

cp -r data_and_config/ training_code/

Then, go to "training_code" folder and train any model that starts with "LADAN+". An example likes:

cd training_code
python LADAN+Topjudge_small.py --config CONFIG_FILE --gpu GPU_ID

where "CONFIG_FILE" corredponds to path of files in the "config" folder and the gpu config is necessary.

References

BibTex:

@inproceedings{xu-etal-2020-distinguish,
    title = {Distinguish Confusing Law Articles for Legal Judgment Prediction},
    author = {Xu, Nuo  and
      Wang, Pinghui  and
      Chen, Long  and
      Pan, Li  and
      Wang, Xiaoyan  and
      Zhao, Junzhou},
    booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
    year = {2020},
    doi = {10.18653/v1/2020.acl-main.280},
    pages = {3086--3095}
}