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License: MIT Python: 3.8+ arXiv Loader: Hugging Face Datasets

Graph-augmented Dense Statute Retriever

This repository contains the code for reproducing the experimental results presented in the EACL 2023 paper "Finding the Law: Enhancing Statutory Article Retrieval via Graph Neural Networks" by Antoine Louis, Gijs van Dijck and Jerry Spanakis.

Statutory article retrieval (SAR), the task of retrieving statute law articles relevant to a legal question, is a promising application of legal text processing. In particular, high-quality SAR systems can improve the work efficiency of legal professionals and provide basic legal assistance to citizens in need at no cost. Unlike traditional ad-hoc information retrieval, where each document is considered a complete source of information, SAR deals with texts whose full sense depends on complementary information from the topological organization of statute law. While existing works ignore these domain-specific dependencies, we propose a novel graph-augmented dense statute retriever (G-DSR) model that incorporates the structure of legislation via a graph neural network to improve dense retrieval performance. Experimental results show that our approach outperforms strong retrieval baselines on a real-world expert-annotated SAR dataset.

Documentation

Detailed documentation on the dataset and how to reproduce the main experimental results can be found here.

Citation

For attribution in academic contexts, please cite this work as:

@inproceedings{louis2023finding,
  title = {Finding the Law: Enhancing Statutory Article Retrieval via Graph Neural Networks},
  author = {Louis, Antoine and van Dijck, Gijs and Spanakis, Gerasimos},
  booktitle = {Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics},
  month = may,
  year = {2023},
  address = {Dubrovnik, Croatia},
  publisher = {Association for Computational Linguistics},
  url = {https://aclanthology.org/2023.eacl-main.203/},
  pages = {2753–2768},
}

License

This repository is MIT-licensed.