Learning Robust Pair Confidence for Multimodal Emotion-Cause Pair Extraction

2026-06-17 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

Multimodal emotion-cause pair extraction (MECPE) often suffers from "pair-confidence brittleness," where existing pair scorers treat links independently, leading to under-constrained relative confidence among competing causes. This vulnerability allows gold pairs to remain close to hard negatives. To address this, the Robust Pair Confidence Learning (RPCL) framework is proposed. RPCL is a training-only method that encourages pair confidence to be both discriminative and stable by enforcing a confidence-difference margin between gold pairs and row-wise hard negatives, and aligning clean predictions with those from partially corrupted contextual views. At inference, the original scorer and decoding pipeline are used. RPCL improved the three-seed mean Pair F1 by 2.58 to 2.83 percentage points and mean Pair AUPRC on ECF, MECAD, and MEC4 datasets in the full text-audio-video setting.

Key takeaway

For machine learning engineers developing multimodal emotion-cause pair extraction (MECPE) systems, consider integrating Robust Pair Confidence Learning (RPCL). This training-only framework significantly improves Pair F1 and AUPRC by explicitly shaping pair confidence to be discriminative and stable. You can enhance your models' robustness by adopting RPCL's margin constraints and prediction alignment techniques, leading to more reliable emotion-cause linking without altering inference pipelines.

Key insights

Robustly shaping pair confidence, by ensuring discriminative and stable predictions, is a highly effective training strategy for multimodal emotion-cause pair extraction.

Principles

• Pair-level cross entropy can lead to pair-confidence brittleness.
• Discriminative and stable pair confidence improves MECPE performance.
• Explicitly shaping pair confidence is an effective training strategy.

Method

RPCL separates gold pairs from hard negatives via a confidence-difference margin constraint and aligns clean pair predictions with those from partially corrupted contextual views.

In practice

• Apply RPCL to enhance MECPE model robustness.
• Implement confidence-difference margins for gold-negative separation.
• Align predictions across clean and corrupted data views.

Topics

• Multimodal Emotion-Cause Pair Extraction
• RPCL Framework
• Pair Confidence Learning
• Deep Learning
• Natural Language Processing
• Audio-Video Processing

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.