No Argument Left Behind: Overlapping Chunks for Faster Processing of Arbitrarily Long Legal Texts
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Abstract (arXiv)
In a context where the Brazilian judiciary system, the largest in the world, faces a crisis due to the slow processing of millions of cases, it becomes imperative to develop efficient methods for analyzing legal texts. We introduce uBERT, a hybrid model that combines Transformer and Recurrent Neural Network architectures to effectively handle long legal texts. Our approach processes the full text regardless of its length while maintaining reasonable computational overhead. Our experiments demonstrate that uBERT achieves superior performance compared to BERT+LSTM when overlapping input is used and is significantly faster than ULMFiT for processing long legal documents.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 16 of its 24 references that are in the map. First posted on arXiv 24 Oct 2024.
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16 of 24 references.
References · 16
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2020Longformer: The Long-Document Transformercited by 3,413
- 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
- 2020Big Bird: Transformers for Longer Sequencescited by 1,684
- 2019BERT Rediscovers the Classical NLP Pipelinecited by 1,059
- 2019Hierarchical Transformers for Long Document Classificationcited by 104
- 2020A Survey on Text Classification: From Shallow to Deep Learningcited by 80
- 2021ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanationcited by 78
- 2018Universal Language Model Fine-tuning for Text Classificationcited by 52
- 2022A Survey on Legal Judgment Prediction: Datasets, Metrics, Models and Challengescited by 50
- 2023Natural Language Processing in the Legal Domaincited by 38
- 2019Long-length Legal Document Classificationcited by 10
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