activity
20232026
most citedEnhancing Pre-Trained Language Models with Sentence Position Embeddings for Rhetorical Roles Recognition in Legal Opinions

3 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2026

Semantic Reranking at Inference Time for Hard Examples in Rhetorical Role Labeling

Anas Belfathi, Nicolas Hernandez, Laura Monceaux +2

Rhetorical Role Labeling (RRL) assigns a functional role to each sentence in a document and is widely used in legal, medical, and scientific domains. While language models (LMs) ac…

cs.CL2026

Coupling Local Context and Global Semantic Prototypes via a Hierarchical Architecture for Rhetorical Roles Labeling

Anas Belfathi, Nicolas Hernandez, Laura Monceaux +4

Rhetorical Role Labeling (RRL) identifies the functional role of each sentence in a document, a key task for discourse understanding in domains such as law and medicine. While hier…

cs.CL2024

Language Model Adaptation to Specialized Domains through Selective Masking based on Genre and Topical Characteristics

Anas Belfathi, Ygor Gallina, Nicolas Hernandez +2

Recent advances in pre-trained language modeling have facilitated significant progress across various natural language processing (NLP) tasks. Word masking during model training co…

cs.CL20231 cited

Harnessing GPT-3.5-turbo for Rhetorical Role Prediction in Legal Cases

Anas Belfathi, Nicolas Hernandez, Laura Monceaux

We propose a comprehensive study of one-stage elicitation techniques for querying a large pre-trained generative transformer (GPT-3.5-turbo) in the rhetorical role prediction task…

cs.CL20233 cited

Enhancing Pre-Trained Language Models with Sentence Position Embeddings for Rhetorical Roles Recognition in Legal Opinions

Anas Belfathi, Nicolas Hernandez, Laura Monceaux

The legal domain is a vast and complex field that involves a considerable amount of text analysis, including laws, legal arguments, and legal opinions. Legal practitioners must ana…