Applying NLP to Support Legal Decision-making in Administrative Appeal Boards in the EU

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While Natural Language Processing (NLP) is being applied in an increasing number of contexts, including law, it remains a difficult task to leverage NLP for the purpose of real-life support of legal decision-making. This is because 1) legal-decision making must be made in a way that is sensitive not only to legislation but also to evolving case practice (prior decision-making that functions as precedent), 2) legal-decision making is sensitive to open-ended legislative language and shifting factual contexts, 3) traditional methods of NLP are capable of processing long texts, but they are suboptimal compared to novel methods, i.e., transformer-based models, e.g., BERT [1], etc. 4) however the transformer-based models are limited by maximum input lengths, which makes it difficult to apply in real-life scenarios, where legal documents exceed the maximum input length. In this paper, we show how we tackle the problem of providing NLP-based intelligence support to legal decision-makers in a real-world setting using transformer-based NLP.

Original languageEnglish
JournalCEUR Workshop Proceedings
Pages (from-to)103-110
Number of pages8
Publication statusPublished - 2023
Event6th Workshop on Automated Semantic Analysis of Information in Legal Text, ASAIL 2023 - Braga, Portugal
Duration: 23 Sep 2023 → …


Conference6th Workshop on Automated Semantic Analysis of Information in Legal Text, ASAIL 2023
Period23/09/2023 → …

Bibliographical note

Publisher Copyright:
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

    Research areas

  • automation bias, decision support, legal decision-making, Legal information retrieval, NLP, public administration


ID: 368339915