# Automatic Anonymization of Swiss Federal Supreme Court Rulings

arXiv: https://arxiv.org/abs/2310.04632 · HTML: https://mlsanity.com/paper/2310.04632/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 44 references that are in the map.
- First posted on arXiv 7 Oct 2023.

## Builds on (most cited first, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Reformer: The Efficient Transformer](https://mlsanity.com/paper/2001.04451/) (2020)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset](https://mlsanity.com/paper/2104.08671/) (2021)
- [German's Next Language Model](https://mlsanity.com/paper/2010.10906/) (2020)
- [Should You Mask 15% in Masked Language Modeling?](https://mlsanity.com/paper/2202.08005/) (2022)
- [UNKs Everywhere: Adapting Multilingual Language Models to New Scripts](https://mlsanity.com/paper/2012.15562/) (2020)
- [Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset](https://mlsanity.com/paper/2207.00220/) (2022)
- [LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domain](https://mlsanity.com/paper/2301.13126/) (2023)
- [Swiss-Judgment-Prediction: A Multilingual Legal Judgment Prediction Benchmark](https://mlsanity.com/paper/2110.00806/) (2021)
- [MultiLegalPile: A 689GB Multilingual Legal Corpus](https://mlsanity.com/paper/2306.02069/) (2023)
- [A Dataset of German Legal Documents for Named Entity Recognition](https://mlsanity.com/paper/2003.13016/) (2020)
- [An Empirical Study on Cross-X Transfer for Legal Judgment Prediction](https://mlsanity.com/paper/2209.12325/) (2022)
- [One Law, Many Languages: Benchmarking Multilingual Legal Reasoning for Judicial Support](https://mlsanity.com/paper/2306.09237/) (2023)
- [BudgetLongformer: Can we Cheaply Pretrain a SotA Legal Language Model From Scratch?](https://mlsanity.com/paper/2211.17135/) (2022)
- [LegalRelectra: Mixed-domain Language Modeling for Long-range Legal Text Comprehension](https://mlsanity.com/paper/2212.08204/) (2022)
- [MultiLegalSBD: A Multilingual Legal Sentence Boundary Detection Dataset](https://mlsanity.com/paper/2305.01211/) (2023)
- [Anonymity at Risk? Assessing Re-Identification Capabilities of Large Language Models](https://mlsanity.com/paper/2308.11103/) (2023)
- [German BERT Model for Legal Named Entity Recognition](https://mlsanity.com/paper/2303.05388/) (2023)
- [Resolving Legalese: A Multilingual Exploration of Negation Scope Resolution in Legal Documents](https://mlsanity.com/paper/2309.08695/) (2023)

## Built on by (most cited first, 3 of 3)

- [A Survey on Current Trends and Recent Advances in Text Anonymization](https://mlsanity.com/paper/2508.21587/) (2025)
- [Thunder-DeID: Accurate and Efficient De-identification Framework for Korean Court Judgments](https://mlsanity.com/paper/2506.15266/) (2025)
- [LLM Anonymization Against Agentic Re-Identification](https://mlsanity.com/paper/2605.30848/) (2026)

## Abstract (arXiv)

Releasing court decisions to the public relies on proper anonymization to protect all involved parties, where necessary. The Swiss Federal Supreme Court relies on an existing system that combines different traditional computational methods with human experts. In this work, we enhance the existing anonymization software using a large dataset annotated with entities to be anonymized. We compared BERT-based models with models pre-trained on in-domain data. Our results show that using in-domain data to pre-train the models further improves the F1-score by more than 5% compared to existing models. Our work demonstrates that combining existing anonymization methods, such as regular expressions, with machine learning can further reduce manual labor and enhance automatic suggestions.