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

HFEPX Relevance Assessment

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Field Provenance & Confidence

Each key protocol field shows extraction state, confidence band, and data source so you can decide whether to trust it directly or validate from full text.

Protocol And Measurement Signals

To address this limitation, we propose two prototype-based methods that integrate local context with global representations. HFEPX signals include Automatic Metrics with confidence 0.35. Updated from current HFEPX corpus.

Generated Mar 14, 2026, 6:23 AM · Grounded in abstract + metadata only

Research Summary

Contribution Summary

• To address this limitation, we propose two prototype-based methods that integrate local context with global representations.
• Given the scarcity of RRL resources, we introduce SCOTUS-Law, the first dataset of U.S.
• Experiments on legal, medical, and scientific benchmarks show consistent improvements over strong baselines, with 4 Macro-F1 gains on low-frequency roles.

Why It Matters For Eval

• Experiments on legal, medical, and scientific benchmarks show consistent improvements over strong baselines, with 4 Macro-F1 gains on low-frequency roles.
• We further analyze the implications in the era of Large Language Models and complement our findings with expert evaluation.

Researcher Checklist

• Gap: Human feedback protocol is explicit

  No explicit human feedback protocol detected.

• Pass: Evaluation mode is explicit

  Detected: Automatic Metrics

• Gap: Quality control reporting appears

  No calibration/adjudication/IAA control explicitly detected.

• Gap: Benchmark or dataset anchors are present

  No benchmark/dataset anchor extracted from abstract.

• Pass: Metric reporting is present

  Detected: f1, f1 macro

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