---
title: "A Novel Synthetic Dataset for Few-Shot Legal — Nouri 2026"
description: "Relation extraction (RE) in German legal texts is hard to develop for three reasons."
authors: ["Shiva Banasaz Nouri", "Elena Leitner", "Julian Moreno-Schneider", "Georg Rehm"]
year: 2026
venue: "Proceedings of the Fifteenth Language Resources and Evaluation Conference"
source: "https://aclanthology.org/2026.lrec-1.830/"
doi: "10.63317/5oaz3mdtekn9"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/nouri-2026-novel-synthetic-dataset-few-shot/"
---
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# A Novel Synthetic Dataset for Few-Shot Legal Relation Extraction in German

**Authors:** Shiva Banasaz Nouri, Elena Leitner, Julian Moreno-Schneider, [Georg Rehm](https://md.research.elamur.ai/free/authors/georg-rehm/) · **Year:** 2026 · **Venue:** Proceedings of the Fifteenth Language Resources and Evaluation Conference · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.lrec-1.830/) · [PDF](https://aclanthology.org/2026.lrec-1.830.pdf) · [DOI 10.63317/5oaz3mdtekn9](https://doi.org/10.63317/5oaz3mdtekn9) · [OpenAlex](https://openalex.org/W7160139128)

**Citations:** 0 citations (openalex-snapshot · 2026-10-14)

## Summary
Relation extraction (RE) in German legal texts is hard to develop for three reasons. Annotated corpora are scarce. Privacy rules such as GDPR block the sharing of real case material. Regulations like the EU AI Act constrain the use of large hosted LLMs. To address this, the authors build a synthetic, privacy-preserving German legal RE dataset. GPT-4o writes formal legal-style template sentences containing typed placeholders, which are then filled programmatically with entities drawn from curated, manually refined lists. Real-looking names are inserted only after generation, so no actual case data can leak, and gold annotations (spans, entity types, relation labels) are produced automatically. The data is stored in FewRel-style JSON for episodic training. As benchmarks, they test two few-shot approaches on German BERT encoders: a MAML variant enriched with relation descriptions, and a Prototypical Network variant trained with a supervised contrastive objective plus curriculum-aware fusion of descriptions. The curriculum-aware Proto-Contrastive model reaches an F1 of 99.83%. The resource fills a gap as the first public FewRel-style dataset for German legal RE.

## Key points
- The pipeline uses GPT-4o to produce only 5–8 templates per prompt, which limits hallucination. Placeholders are filled from entity lists of roughly 40 items each, balanced for gender, ethnic background, organisation types and areas of law.
- The dataset covers six relation types: Represented by, Filed lawsuit against, Accused of, Located at, Occurred on and Fine to be paid. Entity placeholders include PER, AN, ORG, UN, GRT, LD, ST, GS, VO, VT, RS, DAT and GLD.
- The models are a description-enhanced MAML and a Prototypical Network variant that pairs a supervised contrastive objective with curriculum scheduling. Baselines include zero-shot embedding similarity and vanilla Prototypical Networks.
- The best result is the curriculum-aware Proto-Contrastive model, at F1 = 99.83% on the synthetic data.
- The dataset is released under the MIT license (github.com/shivanouri/legal_relation_dataset), with code at github.com/shivanouri/legal-fewrel. The paper appeared at LREC 2026.

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

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/) · [Law](https://md.research.elamur.ai/free/shelves/law/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{nouri2026novel,
  title = {{A Novel Synthetic Dataset for Few-Shot Legal Relation Extraction in German}},
  author = {Nouri, Shiva Banasaz and Leitner, Elena and Moreno-Schneider, Julian and Rehm, Georg},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/5oaz3mdtekn9},
  url = {https://doi.org/10.63317/5oaz3mdtekn9}
}
```

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