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JuriFindIT: an Italian legal retrieval dataset

Paper · 2026 · Niko Dalla Noce, Davide Colla, Sina Farhang Doust et al.

Summary

Statutory article retrieval (SAR) means finding the legislative provisions that answer a natural-language legal question. It is difficult because everyday wording differs from legal terminology, and because statutes depend on each other through hierarchy and citations. No SAR resource existed for Italian. The authors built JuriFindIT by parsing Akoma Ntoso files from European and Italian sources, including laws, decrees, regulations and authority guidelines. They also extracted cross-article references automatically with an XLM-RoBERTa tagger. Legal experts wrote questions in four areas and linked each one to its relevant articles. A large set of synthetic questions supplements the expert ones.

On top of this data, the authors fine-tuned a dense retriever (DAR) and combined it with a GNN-based second stage (DAR+GIR) that runs over a heterogeneous legislative graph. This pipeline outperforms earlier SAR systems on BSARD and LLeQA. The work matters for two reasons: it extends SAR to a new jurisdiction and language, and it is the first SAR dataset to explicitly encode references between articles.

Key points

  • Dataset scale: JuriFindIT contains 895 questions authored by experts plus 169,301 synthetic questions, all mapped to over 23,000 articles. It covers civil law, criminal law, anti-money laundering/counter-terrorism, and privacy.
  • Graph construction:
    • Nodes are either hierarchical (roots, chapters, sections) or content nodes holding article text.
    • Edges are either structural or reference edges.
    • A synthetic super-root joins all files into one graph.
  • References: Cross-references were extracted with a proprietary Aptus.AI model, based on XLM-RoBERTa with BIO tagging, that detects, segments and classifies references.
  • BSARD test set:
    • DAR (305M parameters) reaches recall@100 of 88.4 and mAP of 59.7, against 82.7 and 35.3 for DSR.
    • DAR+GIR reaches mAP of 60.9, against 47.1 for G-DSR.
  • LLeQA validation set:
    • DAR improves recall@5 from 48.6 (CamemBERT) to 59.2.
    • DAR+GIR achieves MRR@10 of 73.6.
  • Limitations:
    • All annotation was done by a single expert team, without independent cross-verification.
    • Adding more synthetic queries can lower retrieval quality.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of PisaPisa · Italy25 works · 3,842 citations

 Fields, topics and concepts

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksSocial Sciences · 1,772 worksInformation Systems · 1,437 worksPolitical Science and International Relations · 182 works

🧭 Topics 2Research subjects and languages the work is about.Information retrieval & search · 473 worksLegal AI · 134 works

Cite

Cite as Niko Dalla Noce, Davide Colla, Sina Farhang Doust, Lorenzo De Mattei and Davide Bacciu. 2026. JuriFindIT: an Italian legal retrieval dataset. Findings of the Association for Computational Linguistics: EACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-eacl.221

@inproceedings{noce2026jurifindit,
  title = {{JuriFindIT: an Italian legal retrieval dataset}},
  author = {Niko Dalla Noce, Davide Colla, Sina Farhang Doust, Lorenzo {De Mattei} and Davide Bacciu},
  booktitle = {Findings of the Association for Computational Linguistics: EACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-eacl.221},
  url = {https://doi.org/10.18653/v1/2026.findings-eacl.221}
}

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