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About this paper
Zhuangzhuang Pan, Yan Xia, Chee Seng Chan
This research from Universiti Malaya clarifies that current Emotion-Cause Pair Extraction (ECPE) models primarily excel at direct-cause extraction, not comprehensive emotion explanation, by introducing a diagnostic dataset, IEMO-MECP, which distinguishes between direct causes and non-triggering contextual evidence. The study demonstrates that existing models frequently misclassify crucial contextual support as irrelevant, and their binary performance metrics can be heavily influenced by superficial 'shortcut' cues rather than a deep understanding of explanatory evidence.
Key insights
- The binary ECPE label space compresses a more nuanced evidential structure; 'emo-context' represents a distinct discourse role (interpretive support) that is neither a direct cause nor irrelevant, often crossing both positive and negative binary boundaries.
- Binary ECPE models struggle to identify and differentiate 'emo-context,' frequently misclassifying it as 'non-pair,' indicating a lack of a dedicated 'context channel' and highlighting a fundamental limitation in their explanatory capabilities.
- Performance metrics for binary ECPE can be significantly inflated by models exploiting 'shortcut-compatible' cues like lexical overlap and spatial proximity, rather than genuinely comprehending structurally harder, evidence-supported but non-local or non-causal relations.
Summary by alphaXiv.
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