# HiCuLR: Hierarchical Curriculum Learning for Rhetorical Role Labeling of Legal Documents

arXiv: https://arxiv.org/abs/2409.18647 · HTML: https://mlsanity.com/paper/2409.18647/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 43 references that are in the map.
- First posted on arXiv 27 Sep 2024.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [On The Power of Curriculum Learning in Training Deep Networks](https://mlsanity.com/paper/1904.03626/) (2019)
- [Competence-based Curriculum Learning for Neural Machine Translation](https://mlsanity.com/paper/1903.09848/) (2019)
- [Curriculum Learning and Minibatch Bucketing in Neural Machine Translation](https://mlsanity.com/paper/1707.09533/) (2017)
- [An Empirical Exploration of Curriculum Learning for Neural Machine Translation](https://mlsanity.com/paper/1811.00739/) (2018)
- [Identification of Rhetorical Roles of Sentences in Indian Legal Judgments](https://mlsanity.com/paper/1911.05405/) (2019)
- [Curriculum Learning for Dense Retrieval Distillation](https://mlsanity.com/paper/2204.13679/) (2022)
- [Does the Order of Training Samples Matter? Improving Neural Data-to-Text Generation with Curriculum Learning](https://mlsanity.com/paper/2102.03554/) (2021)
- [Dialogue Response Selection with Hierarchical Curriculum Learning](https://mlsanity.com/paper/2012.14756/) (2020)
- [Semantic Segmentation of Legal Documents via Rhetorical Roles](https://mlsanity.com/paper/2112.01836/) (2021)
- [Corpus for Automatic Structuring of Legal Documents](https://mlsanity.com/paper/2201.13125/) (2022)
- [Label-similarity Curriculum Learning](https://mlsanity.com/paper/1911.06902/) (2019)
- [Hierarchical Curriculum Learning for AMR Parsing](https://mlsanity.com/paper/2110.07855/) (2021)
- [From Easy to Hard: A Dual Curriculum Learning Framework for Context-Aware Document Ranking](https://mlsanity.com/paper/2208.10226/) (2022)
- [CuSINeS: Curriculum-driven Structure Induced Negative Sampling for Statutory Article Retrieval](https://mlsanity.com/paper/2404.00590/) (2024)
- [Mind Your Neighbours: Leveraging Analogous Instances for Rhetorical Role Labeling for Legal Documents](https://mlsanity.com/paper/2404.01344/) (2024)
- [Curriculum-guided Abstractive Summarization for Mental Health Online Posts](https://mlsanity.com/paper/2302.00954/) (2023)
- [Joint Span Segmentation and Rhetorical Role Labeling with Data Augmentation for Legal Documents](https://mlsanity.com/paper/2302.06448/) (2023)

## Built on by (most cited first, 6 of 6)

- [LegalSeg: Unlocking the Structure of Indian Legal Judgments Through Rhetorical Role Classification](https://mlsanity.com/paper/2502.05836/) (2025)
- [Segmentation and Processing of German Court Decisions from Open Legal Data](https://mlsanity.com/paper/2601.01449/) (2026)
- [TathyaNyaya and FactLegalLlama: Advancing Factual Judgment Prediction and Explanation in the Indian Legal Context](https://mlsanity.com/paper/2504.04737/) (2025)
- [Coupling Local Context and Global Semantic Prototypes via a Hierarchical Architecture for Rhetorical Roles Labeling](https://mlsanity.com/paper/2603.03856/) (2026)
- [Structured Legal Document Generation in India: A Model-Agnostic Wrapper Approach with VidhikDastaavej](https://mlsanity.com/paper/2504.03486/) (2025)
- [Semantic Reranking at Inference Time for Hard Examples in Rhetorical Role Labeling](https://mlsanity.com/paper/2605.18007/) (2026)

## Abstract (arXiv)

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.