Summary of No Argument Left Behind: Overlapping Chunks For Faster Processing Of Arbitrarily Long Legal Texts, by Israel Fama et al.
No Argument Left Behind: Overlapping Chunks for Faster Processing of Arbitrarily Long Legal Texts
by Israel Fama, Bárbara Bueno, Alexandre Alcoforado, Thomas Palmeira Ferraz, Arnold Moya, Anna Helena Reali Costa
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces uBERT, a hybrid model combining Transformer and Recurrent Neural Network architectures, designed to efficiently analyze long legal texts. By processing full texts regardless of length while maintaining reasonable computational overhead, uBERT outperforms BERT+LSTM in overlapping input scenarios and is significantly faster than ULMFiT for processing long legal documents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a world where the Brazilian judiciary system faces a crisis due to slow case processing, developing efficient methods for analyzing legal texts is crucial. The paper introduces uBERT, a hybrid model that combines Transformer and Recurrent Neural Network architectures. This approach processes full texts regardless of length while maintaining reasonable computational overhead. It’s like having a superpower for understanding long documents! |
Keywords
» Artificial intelligence » Bert » Lstm » Neural network » Transformer
