Causal Emotion Recognition in Conversation: Context Saturation and Discourse-Marker Evidence

Researchers conducted a systematic study on emotion recognition in conversation using the IEMOCAP dataset, identifying that conversational context dominates performance but saturates within 10-30 preceding turns. The study reveals that hierarchical sentence representations and external affective lexicons provide minimal additional benefit, while discourse-marker analysis shows sadness correlates with reduced left-periphery markers, suggesting emotional states vary in context-dependency.

This research addresses fundamental questions about how machines recognize human emotions in conversational settings, moving beyond black-box accuracy metrics to examine which modeling components actually drive improvements. The work employs rigorous methodology—controlled ablations with multiple random seeds and corrected significance testing—establishing that conversational history is the critical factor, not sophisticated intra-utterance processing. The finding that 90% of performance gains concentrate in the most recent 10-30 turns has practical implications: computational efficiency can be prioritized over extended historical context without sacrificing accuracy.

The study challenges conventional wisdom in NLP research. The fact that pretrained language models already capture sufficient affective signals questions the value of augmenting systems with specialized lexicons, a common practice in older emotion-recognition systems. More intriguingly, hierarchical sentence encoders—architecturally expensive components designed to capture fine-grained linguistic structure—provide no measurable benefit once conversational context exists. This suggests that turn-level information masks utterance-internal patterns, making complex intra-utterance processing redundant.

The linguistic analysis linking discourse markers to emotional states offers interpretability gains often missing in deep learning approaches. The correlation between sadness and reduced left-periphery markers (21.9% versus 28-32% for other emotions) aligns with psycholinguistic theory and explains why sad utterances benefit most from context (+22 percentage points). This connection between quantifiable linguistic patterns and model behavior creates a feedback loop where linguistic insights improve system design and model results validate linguistic hypotheses, advancing both computational and cognitive understanding of emotion expression.

• →Conversational context dominates emotion recognition but saturates quickly within 10-30 preceding turns, enabling efficient computational design.
• →Hierarchical sentence representations provide no benefit once turn-level conversational context is available, suggesting redundancy in complex intra-utterance architectures.
• →Pretrained language models already capture sufficient affective signals, making external lexicon augmentation unnecessary for competitive performance.
• →Sadness shows reduced left-periphery discourse-marker usage (21.9%) compared to other emotions (28-32%), correlating with higher context-dependency in recognition.
• →Strictly causal models achieve strong performance (82.69% 4-way F1) without access to future turns, validating practical deployment constraints.