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AI Research
arXiv CS.AI·May 26·5 min read
Temporal Concept Drift in Legal Judgment Prediction: Neural Baselines Across Three Epochs of Ukrainian Court Decisions
TL;DR
Legal NLP models often assume that legal language remains constant over time, which this study challenges. Four transformer encoders, including XLM-RoBERTa, were fine-tuned on Ukrainian court decisions from three distinct geopolitical periods.
✦ Why It Matters
Engineers can leverage insights on temporal concept drift to improve legal NLP model robustness over time.
Key Takeaways
- Evaluate models on temporal data to understand performance degradation.
- Implement continual learning strategies to retain knowledge over time.
- Consider legal-domain pretraining to reduce degradation effects.
- Utilize the publicly available dataset for further research and model training.
How It Works
The study employs transformer models, specifically XLM-RoBERTa, to analyze legal text across different time periods. By training on one epoch and testing on others, the research reveals how legal language evolves, impacting model performance.
The use of continual learning techniques allows models to adapt and retain knowledge from previous epochs, addressing the issue of catastrophic forgetting.