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uOttawa at LegalLens-2024: Transformer-based Classification Experiments

arXiv 2410.21139 ↗First posted 28 Oct 2024cs.CLcited by 0

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Abstract (arXiv)

This paper presents the methods used for LegalLens-2024 shared task, which focused on detecting legal violations within unstructured textual data and associating these violations with potentially affected individuals. The shared task included two subtasks: A) Legal Named Entity Recognition (L-NER) and B) Legal Natural Language Inference (L-NLI). For subtask A, we utilized the spaCy library, while for subtask B, we employed a combined model incorporating RoBERTa and CNN. Our results were 86.3% in the L-NER subtask and 88.25% in the L-NLI subtask. Overall, our paper demonstrates the effectiveness of transformer models in addressing complex tasks in the legal domain. The source code for our implementation is publicly available at https://github.com/NimaMeghdadi/uOttawa-at-LegalLens-2024-Transformer-based-Classification

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 18 references that are in the map. First posted on arXiv 28 Oct 2024.

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References · 6

  1. 2017A Broad-Coverage Challenge Corpus for Sentence Understanding through Inferencecited by 2,665
  2. 2015A large annotated corpus for learning natural language inferencecited by 1,837
  3. 2021DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharingcited by 1,219
  4. 2019Adversarial NLI: A New Benchmark for Natural Language Understandingcited by 798
  5. 2024LegalLens: Leveraging LLMs for Legal Violation Identification in Unstructured Textcited by 12
  6. 2024LegalLens Shared Task 2024: Legal Violation Identification in Unstructured Textcited by 5

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