mlsanityData to 14 Oct 2026

They Are Not the Same: Direct Causes Are Not Grounded Emotion Explanations

arXiv 2605.25208 ↗First posted 24 May 2026cs.CLcited by 0

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Abstract (arXiv)

Emotion-Cause Pair Extraction (ECPE) was introduced to explain why an emotion occurs, but this goal is now often reduced to binary pair/non-pair prediction. This proxy is useful for direct-cause extraction, yet easy to over-read as evidence grounded emotion explanation. We show that this interpretation is only partially valid. In IEMO-MECP, 90.9% of original positives remain emo-cause and 95.0% of original negatives remain non-pair, confirming that the binary ECPE task is largely preserved. The problem is that direct triggers alone do not constitute a grounded explanation. Emo-context, an utterance that helps interpret a target emotion without directly causing it, appears on both sides of the original boundary and is enriched near binary uncertainty, showing that the binary boundary has no stable place for such discourse evidence. Across evaluated ECPE models, direct triggers are recovered more reliably than contextual support. Under shortcut pressure, this imbalance becomes consequential. Binary-trained models assign higher pair scores to nearby lexically similar non-pair candidates than to evidence supported but structurally harder emo-cause and emo-context pairs. Thus, pair scores can reward convenient attributions over grounded explanations. High binary ECPE performance indicates that a model can identify direct triggers; it does not indicate that the model has explained the emotion. Code is publicly available at https://github.com/panzhzh/ECPExsame.

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References · 13

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  3. 2021WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processingcited by 1,657
  4. 2019Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Textscited by 83
  5. 2020Recognizing Emotion Cause in Conversationscited by 66
  6. 2022Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Predictioncited by 46
  7. 2024Revisiting Multimodal Emotion Recognition in Conversation from the Perspective of Graph Spectrumcited by 33
  8. 2021Multimodal Emotion-Cause Pair Extraction in Conversationscited by 31
  9. 2023Conversation Understanding using Relational Temporal Graph Neural Networks with Auxiliary Cross-Modality Interactioncited by 18
  10. 2021Position Bias Mitigation: A Knowledge-Aware Graph Model for Emotion Cause Extractioncited by 17
  11. 2022Knowledge-Bridged Causal Interaction Network for Causal Emotion Entailmentcited by 16
  12. 2024SemEval-2024 Task 3: Multimodal Emotion Cause Analysis in Conversationscited by 12
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  1. 2025M3HG: Multimodal, Multi-scale, and Multi-type Node Heterogeneous Graph for Emotion Cause Triplet Extraction in Conversationscited by 3

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