# An Empirical Study on Cross-X Transfer for Legal Judgment Prediction

arXiv: https://arxiv.org/abs/2209.12325 · HTML: https://mlsanity.com/paper/2209.12325/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 49 references that are in the map.
- First posted on arXiv 25 Sep 2022.

## 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)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [AdapterFusion: Non-Destructive Task Composition for Transfer Learning](https://mlsanity.com/paper/2005.00247/) (2020)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer](https://mlsanity.com/paper/2005.00052/) (2020)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [LexGLUE: A Benchmark Dataset for Legal Language Understanding in English](https://mlsanity.com/paper/2110.00976/) (2021)
- [On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines](https://mlsanity.com/paper/2006.04884/) (2020)
- [Neural Legal Judgment Prediction in English](https://mlsanity.com/paper/1906.02059/) (2019)
- [When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset](https://mlsanity.com/paper/2104.08671/) (2021)
- [CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction](https://mlsanity.com/paper/1807.02478/) (2018)
- [XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation](https://mlsanity.com/paper/2104.07412/) (2021)
- [Lawformer: A Pre-trained Language Model for Chinese Legal Long Documents](https://mlsanity.com/paper/2105.03887/) (2021)
- [MultiEURLEX -- A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer](https://mlsanity.com/paper/2109.00904/) (2021)
- [ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation](https://mlsanity.com/paper/2105.13562/) (2021)
- [Swiss-Judgment-Prediction: A Multilingual Legal Judgment Prediction Benchmark](https://mlsanity.com/paper/2110.00806/) (2021)
- [Revisiting Transformer-based Models for Long Document Classification](https://mlsanity.com/paper/2204.06683/) (2022)
- [Predicting the Law Area and Decisions of French Supreme Court Cases](https://mlsanity.com/paper/1708.01681/) (2017)
- [On the Ethical Limits of Natural Language Processing on Legal Text](https://mlsanity.com/paper/2105.02751/) (2021)
- [Realistic Zero-Shot Cross-Lingual Transfer in Legal Topic Classification](https://mlsanity.com/paper/2206.03785/) (2022)

## Built on by (most cited first, 16 of 16)

- [LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domain](https://mlsanity.com/paper/2301.13126/) (2023)
- [MultiLegalPile: A 689GB Multilingual Legal Corpus](https://mlsanity.com/paper/2306.02069/) (2023)
- [Processing Long Legal Documents with Pre-trained Transformers: Modding LegalBERT and Longformer](https://mlsanity.com/paper/2211.00974/) (2022)
- [ClassActionPrediction: A Challenging Benchmark for Legal Judgment Prediction of Class Action Cases in the US](https://mlsanity.com/paper/2211.00582/) (2022)
- [Unsupervised Layer-wise Score Aggregation for Textual OOD Detection](https://mlsanity.com/paper/2302.09852/) (2023)
- [SwiLTra-Bench: The Swiss Legal Translation Benchmark](https://mlsanity.com/paper/2503.01372/) (2025)
- [MultiLegalSBD: A Multilingual Legal Sentence Boundary Detection Dataset](https://mlsanity.com/paper/2305.01211/) (2023)
- [Towards Explainability and Fairness in Swiss Judgement Prediction: Benchmarking on a Multilingual Dataset](https://mlsanity.com/paper/2402.17013/) (2024)
- [Resolving Legalese: A Multilingual Exploration of Negation Scope Resolution in Legal Documents](https://mlsanity.com/paper/2309.08695/) (2023)
- [Automatic Anonymization of Swiss Federal Supreme Court Rulings](https://mlsanity.com/paper/2310.04632/) (2023)
- [Improving Legal Judgement Prediction in Romanian with Long Text Encoders](https://mlsanity.com/paper/2402.19170/) (2024)
- [From Citations to Criticality: Predicting Legal Decision Influence in the Multilingual Swiss Jurisprudence](https://mlsanity.com/paper/2410.13460/) (2024)
- [Multi-Legal-Bench: Evaluating LLMs on Legal Reasoning Across Jurisdictions, Languages, and Legal Traditions](https://mlsanity.com/paper/2605.29738/) (2026)
- [Data Processing for the OpenGPT-X Model Family](https://mlsanity.com/paper/2410.08800/) (2024)
- [Are manual annotations necessary for statutory interpretations retrieval?](https://mlsanity.com/paper/2506.13965/) (2025)
- [Temporal Concept Drift in Legal Judgment Prediction: Neural Baselines Across Three Epochs of Ukrainian Court Decisions](https://mlsanity.com/paper/2605.24452/) (2026)

## Abstract (arXiv)

Cross-lingual transfer learning has proven useful in a variety of Natural Language Processing (NLP) tasks, but it is understudied in the context of legal NLP, and not at all in Legal Judgment Prediction (LJP). We explore transfer learning techniques on LJP using the trilingual Swiss-Judgment-Prediction dataset, including cases written in three languages. We find that cross-lingual transfer improves the overall results across languages, especially when we use adapter-based fine-tuning. Finally, we further improve the model's performance by augmenting the training dataset with machine-translated versions of the original documents, using a 3x larger training corpus. Further on, we perform an analysis exploring the effect of cross-domain and cross-regional transfer, i.e., train a model across domains (legal areas), or regions. We find that in both settings (legal areas, origin regions), models trained across all groups perform overall better, while they also have improved results in the worst-case scenarios. Finally, we report improved results when we ambitiously apply cross-jurisdiction transfer, where we further augment our dataset with Indian legal cases.