This repository contains the code for the paper: Attentive deep neural networks for legal document retrieval
- Data provided by COLIEE Competition
- Pretrained checkpoint can be downloaded here
conda create -n paraformer python=3.6
conda activate paraformer
pip install -r requirements.txt
python train.py \
--data-dir DATA_DIR \
--test-file TEST_FILE \
--max-epochs NUM_EPOCHES
python eval.py \
--data-dir DATA_DIR \
--test-file TEST_FILE \
--checkpoint CHECKPOINT_PATH \
--bm25-top-n TOP_N \
--alpha ALPHA
- Nguyen, H., Phi, M., Ngo, X., Tran, V., Nguyen, L., & Tu, M. (2022). Attentive deep neural networks for legal document retrieval. Artificial Intelligence and Law, 1-30. Springer.
BibTeX:
@article{nguyen2022attentive,
title={Attentive deep neural networks for legal document retrieval},
author={Nguyen, Ha-Thanh and Phi, Manh-Kien and Ngo, Xuan-Bach and Tran, Vu and Nguyen, Le-Minh and Tu, Minh-Phuong},
journal={Artificial Intelligence and Law},
pages={1--30},
year={2022},
publisher={Springer}
}