# Joint Span Segmentation and Rhetorical Role Labeling with Data Augmentation for Legal Documents

arXiv: https://arxiv.org/abs/2302.06448 · HTML: https://mlsanity.com/paper/2302.06448/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 33 references that are in the map.
- First posted on arXiv 13 Feb 2023.

## Builds on (most cited first, 14 of 14)

- [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)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [An Analysis of Simple Data Augmentation for Named Entity Recognition](https://mlsanity.com/paper/2010.11683/) (2020)
- [Identification of Rhetorical Roles of Sentences in Indian Legal Judgments](https://mlsanity.com/paper/1911.05405/) (2019)
- [Corpus for Automatic Structuring of Legal Documents](https://mlsanity.com/paper/2201.13125/) (2022)
- [Hierarchical Neural Networks for Sequential Sentence Classification in Medical Scientific Abstracts](https://mlsanity.com/paper/1808.06161/) (2018)
- [Segmental Recurrent Neural Networks](https://mlsanity.com/paper/1511.06018/) (2015)
- [Unsupervised Data Augmentation with Naive Augmentation and without Unlabeled Data](https://mlsanity.com/paper/2010.11966/) (2020)
- [Hybrid semi-Markov CRF for Neural Sequence Labeling](https://mlsanity.com/paper/1805.03838/) (2018)
- [Segmental Recurrent Neural Networks for End-to-end Speech Recognition](https://mlsanity.com/paper/1603.00223/) (2016)
- [Exploring Segment Representations for Neural Segmentation Models](https://mlsanity.com/paper/1604.05499/) (2016)
- [Neural Semi-Markov Conditional Random Fields for Robust Character-Based Part-of-Speech Tagging](https://mlsanity.com/paper/1808.04208/) (2018)

## Built on by (most cited first, 3 of 3)

- [HiCuLR: Hierarchical Curriculum Learning for Rhetorical Role Labeling of Legal Documents](https://mlsanity.com/paper/2409.18647/) (2024)
- [Mind Your Neighbours: Leveraging Analogous Instances for Rhetorical Role Labeling for Legal Documents](https://mlsanity.com/paper/2404.01344/) (2024)
- [Semantic Reranking at Inference Time for Hard Examples in Rhetorical Role Labeling](https://mlsanity.com/paper/2605.18007/) (2026)

## Abstract (arXiv)

Segmentation and Rhetorical Role Labeling of legal judgements play a crucial role in retrieval and adjacent tasks, including case summarization, semantic search, argument mining etc. Previous approaches have formulated this task either as independent classification or sequence labeling of sentences. In this work, we reformulate the task at span level as identifying spans of multiple consecutive sentences that share the same rhetorical role label to be assigned via classification. We employ semi-Markov Conditional Random Fields (CRF) to jointly learn span segmentation and span label assignment. We further explore three data augmentation strategies to mitigate the data scarcity in the specialized domain of law where individual documents tend to be very long and annotation cost is high. Our experiments demonstrate improvement of span-level prediction metrics with a semi-Markov CRF model over a CRF baseline. This benefit is contingent on the presence of multi sentence spans in the document.