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        <title>HiCuLR: Hierarchical Curriculum Learning for Rhetorical Role Labeling of Legal Documents</title>
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    <abstract>Rhetorical Role Labeling (RRL) of legal documents is pivotal for various downstream tasks such as summarization, semantic case search and argument mining. Existing approaches often overlook the varying difficulty levels inherent in legal document discourse styles and rhetorical roles. In this work, we propose HiCuLR, a hierarchical curriculum learning framework for RRL. It nests two curricula: Rhetorical Role-level Curriculum (RC) on the outer layer and Document-level Curriculum (DC) on the inner layer. DC categorizes documents based on their difficulty, utilizing metrics like deviation from a standard discourse structure and exposes the model to them in an easy-to-difficult fashion. RC progressively strengthens the model to discern coarse-to-fine-grained distinctions between rhetorical roles. Our experiments on four RRL datasets demonstrate the efficacy of HiCuLR, highlighting the complementary nature of DC and RC.</abstract>
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%0 Conference Proceedings
%T HiCuLR: Hierarchical Curriculum Learning for Rhetorical Role Labeling of Legal Documents
%A T.y.s.s, Santosh
%A Isaia, Apolline
%A Hong, Shiyu
%A Grabmair, Matthias
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F t-y-s-s-etal-2024-hiculr
%X Rhetorical Role Labeling (RRL) of legal documents is pivotal for various downstream tasks such as summarization, semantic case search and argument mining. Existing approaches often overlook the varying difficulty levels inherent in legal document discourse styles and rhetorical roles. In this work, we propose HiCuLR, a hierarchical curriculum learning framework for RRL. It nests two curricula: Rhetorical Role-level Curriculum (RC) on the outer layer and Document-level Curriculum (DC) on the inner layer. DC categorizes documents based on their difficulty, utilizing metrics like deviation from a standard discourse structure and exposes the model to them in an easy-to-difficult fashion. RC progressively strengthens the model to discern coarse-to-fine-grained distinctions between rhetorical roles. Our experiments on four RRL datasets demonstrate the efficacy of HiCuLR, highlighting the complementary nature of DC and RC.
%U https://aclanthology.org/2024.findings-emnlp.433
%P 7357-7364

Markdown (Informal)

[HiCuLR: Hierarchical Curriculum Learning for Rhetorical Role Labeling of Legal Documents](https://aclanthology.org/2024.findings-emnlp.433) (T.y.s.s et al., Findings 2024)

• HiCuLR: Hierarchical Curriculum Learning for Rhetorical Role Labeling of Legal Documents (T.y.s.s et al., Findings 2024)

ACL

• Santosh T.y.s.s, Apolline Isaia, Shiyu Hong, and Matthias Grabmair. 2024. HiCuLR: Hierarchical Curriculum Learning for Rhetorical Role Labeling of Legal Documents. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 7357–7364, Miami, Florida, USA. Association for Computational Linguistics.