  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.
