mlsanityData to 7 Oct 2026

ParaLaw Nets -- Cross-lingual Sentence-level Pretraining for Legal Text Processing

arXiv 2106.13403 ↗First posted 25 Jun 2021cs.CL cs.AIcited by 5

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Abstract (arXiv)

Ambiguity is a characteristic of natural language, which makes expression ideas flexible. However, in a domain that requires accurate statements, it becomes a barrier. Specifically, a single word can have many meanings and multiple words can have the same meaning. When translating a text into a foreign language, the translator needs to determine the exact meaning of each element in the original sentence to produce the correct translation sentence. From that observation, in this paper, we propose ParaLaw Nets, a pretrained model family using sentence-level cross-lingual information to reduce ambiguity and increase the performance in legal text processing. This approach achieved the best result in the Question Answering task of COLIEE-2021.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 9 of its 16 references that are in the map. First posted on arXiv 25 Jun 2021.

Builds on

9 of 16 references.

References · 9

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  9. 2020JNLP Team: Deep Learning for Legal Processing in COLIEE 2020cited by 14

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Cited by year

Citing papers per year: 2022: 3; 2023: 0; 2024: 2.

5 papers.

  1. 2022Attentive Deep Neural Networks for Legal Document Retrievalcited by 13
  2. 2022A Summary of the ALQAC 2021 Competitioncited by 9
  3. 2024CAPTAIN at COLIEE 2023: Efficient Methods for Legal Information Retrieval and Entailment Taskscited by 4
  4. 2022Transformer-based Approaches for Legal Text Processingcited by 3
  5. 2024Employing Label Models on ChatGPT Answers Improves Legal Text Entailment Performancecited by 2