Transformer models for legal judgment prediction show up to 27.2 pp macro-F1 forward degradation across three Ukrainian court epochs, with asymmetric transfer, reduced degradation from legal pretraining, and gains from chronological continual learning.
Unsupervised cross-lingual representation learning at scale.Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8440–8451, 2020
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Temporal Concept Drift in Legal Judgment Prediction: Neural Baselines Across Three Epochs of Ukrainian Court Decisions
Transformer models for legal judgment prediction show up to 27.2 pp macro-F1 forward degradation across three Ukrainian court epochs, with asymmetric transfer, reduced degradation from legal pretraining, and gains from chronological continual learning.