New RISE framework improves Rhetorical Role Labeling accuracy on hard examples

Researchers have developed RISE, a novel framework designed to improve the accuracy of Rhetorical Role Labeling (RRL) for challenging text examples. This inference-time semantic reranking approach leverages the semantic meaning of labels to refine predictions from language models, particularly when confidence is low. Experiments across eight datasets and seven language models demonstrated an average improvement of 9.15 macro-F1 points on difficult cases without altering the underlying models. The study also explored manual hardness annotations, finding a moderate agreement with model-based difficulty assessments. AI

IMPACT Enhances the reliability of language models for specialized text analysis tasks like legal and scientific document processing.

RANK_REASON This is a research paper detailing a new method for a specific NLP task. [lever_c_demoted from research: ic=1 ai=1.0]

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• Anas Belfathi
• arXiv
• Cohen's kappa
• Hugging Face
• Rhetorical Role Labeling
• RISE

• paper
• other

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