GCI

Source code and data for Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysis (NAACL 2021 oral paper).

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Dependencies

• Python>=3.7
• Spacy (for word segmentation)
• Numpy
• Pandas
• Sklearn
• Pke (for keyword extraction)
• Py-Causal (for causal discovery)
• Networkx (for analyzing causal graphs)
• Pydot (for saving causal graphs)
• Dowhy (for causal inference)
• Pytorch>=1.1
• Gensim (for loading word vectors)
• Pgmpy (for calculating BIC score)

Prepare Data

The dataset used is provided in data/data.zip. Before running the models, some preprocessing is needed:

• Unzip data/data.zip into data/data.json.
• Download word embedding from https://ai.tencent.com/ailab/nlp/en/data/tencent-ailab-embedding-zh-d200-v0.2.0.tar.gz and extract them into data/.
• Run preprocess.py to prepare data for GCI.

(Optional) If you want to start from the raw data, please run:

• Download original data from https://cail.oss-cn-qingdao.aliyuncs.com/CAIL2018_ALL_DATA.zip and extract them into data/.
• Run prepare_dataset.py to build the dataset and store it in data/data.json.

Run GCI

python gci.py --charge DATASET_NAME --ratio TRAINING_DATA_RATIO

Argment DATASET_NAME is chosen from:

• II-M-N (Personal Injury: Intentional Injury & Murder & Involuntary Manslaughter),
• R-K-S (Violent Acquisition: Robbery & Kidnapping & Seizure),
• F-E (Fraud & Extortion),
• E-MPF (Embezzlement & Misappropriation of Public Funds),
• AP-DD (Abuse of Power & Dereliction of Duty).

And TRAINING_DATA_RATIO is chosen from {0.01, 0.05, 0.1, 0.3, 0.5}.

Integrate GCI with Neural Networks

This part of code is modified from https://github.com/649453932/Chinese-Text-Classification-Pytorch. As causal knowledge is needed, GCI should be executed first.

Impose Strength Constraint

python run_nn.py --model BiLSTM_Att_Cons --charge DATASET_NAME --ratio TRAINING_DATA_RATIO

Leverage Causal Chains

python run_nn.py --model CausalChain --charge DATASET_NAME --ratio TRAINING_DATA_RATIO

Citation

Please cite our paper if this repository inspires your work.

@inproceedings{liu2021everything,
  title={Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysis},
  author={Liu, Xiao and Yin, Da and Feng, Yansong and Wu, Yuting and Zhao, Dongyan},
  booktitle={Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  pages={1928--1941},
  year={2021}
}

Contact

If you have any questions regarding the code, please create an issue or contact the owner of this repository.