@inproceedings{hagag-etal-2024-legallens,
title = "{L}egal{L}ens Shared Task 2024: Legal Violation Identification in Unstructured Text",
author = "Hagag, Ben and
Gil Semo, Gil and
Bernsohn, Dor and
Harpaz, Liav and
Vaezipoor, Pashootan and
Saha, Rohit and
Truskovskyi, Kyryl and
Spanakis, Gerasimos",
editor = "Aletras, Nikolaos and
Chalkidis, Ilias and
Barrett, Leslie and
Goan{\textcommabelow{t}}{\u{a}}, C{\u{a}}t{\u{a}}lina and
Preo{\textcommabelow{t}}iuc-Pietro, Daniel and
Spanakis, Gerasimos",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2024",
month = nov,
year = "2024",
address = "Miami, FL, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.nllp-1.33",
doi = "10.18653/v1/2024.nllp-1.33",
pages = "361--370",
abstract = "This paper presents the results of the LegalLens Shared Task, focusing on detecting legal violations within text in the wild across two sub-tasks: LegalLens-NER for identifying legal violation entities and LegalLens-NLI for associating these violations with relevant legal contexts and affected individuals. Using an enhanced LegalLens dataset covering labor, privacy, and consumer protection domains, 38 teams participated in the task. Our analysis reveals that while a mix of approaches was used, the top-performing teams in both tasks consistently relied on fine-tuning pre-trained language models, outperforming legal-specific models and few-shot methods. The top-performing team achieved a 7.11{\%} improvement in NER over the baseline, while NLI saw a more marginal improvement of 5.7{\%}. Despite these gains, the complexity of legal texts leaves room for further advancements.",
}
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<abstract>This paper presents the results of the LegalLens Shared Task, focusing on detecting legal violations within text in the wild across two sub-tasks: LegalLens-NER for identifying legal violation entities and LegalLens-NLI for associating these violations with relevant legal contexts and affected individuals. Using an enhanced LegalLens dataset covering labor, privacy, and consumer protection domains, 38 teams participated in the task. Our analysis reveals that while a mix of approaches was used, the top-performing teams in both tasks consistently relied on fine-tuning pre-trained language models, outperforming legal-specific models and few-shot methods. The top-performing team achieved a 7.11% improvement in NER over the baseline, while NLI saw a more marginal improvement of 5.7%. Despite these gains, the complexity of legal texts leaves room for further advancements.</abstract>
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%0 Conference Proceedings
%T LegalLens Shared Task 2024: Legal Violation Identification in Unstructured Text
%A Hagag, Ben
%A Gil Semo, Gil
%A Bernsohn, Dor
%A Harpaz, Liav
%A Vaezipoor, Pashootan
%A Saha, Rohit
%A Truskovskyi, Kyryl
%A Spanakis, Gerasimos
%Y Aletras, Nikolaos
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Goan\textcommabelowtă, Cătălina
%Y Preo\textcommabelowtiuc-Pietro, Daniel
%Y Spanakis, Gerasimos
%S Proceedings of the Natural Legal Language Processing Workshop 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, FL, USA
%F hagag-etal-2024-legallens
%X This paper presents the results of the LegalLens Shared Task, focusing on detecting legal violations within text in the wild across two sub-tasks: LegalLens-NER for identifying legal violation entities and LegalLens-NLI for associating these violations with relevant legal contexts and affected individuals. Using an enhanced LegalLens dataset covering labor, privacy, and consumer protection domains, 38 teams participated in the task. Our analysis reveals that while a mix of approaches was used, the top-performing teams in both tasks consistently relied on fine-tuning pre-trained language models, outperforming legal-specific models and few-shot methods. The top-performing team achieved a 7.11% improvement in NER over the baseline, while NLI saw a more marginal improvement of 5.7%. Despite these gains, the complexity of legal texts leaves room for further advancements.
%R 10.18653/v1/2024.nllp-1.33
%U https://aclanthology.org/2024.nllp-1.33
%U https://doi.org/10.18653/v1/2024.nllp-1.33
%P 361-370
Markdown (Informal)
[LegalLens Shared Task 2024: Legal Violation Identification in Unstructured Text](https://aclanthology.org/2024.nllp-1.33) (Hagag et al., NLLP 2024)
ACL
- Ben Hagag, Gil Gil Semo, Dor Bernsohn, Liav Harpaz, Pashootan Vaezipoor, Rohit Saha, Kyryl Truskovskyi, and Gerasimos Spanakis. 2024. LegalLens Shared Task 2024: Legal Violation Identification in Unstructured Text. In Proceedings of the Natural Legal Language Processing Workshop 2024, pages 361–370, Miami, FL, USA. Association for Computational Linguistics.