Causal Emotion Recognition in Conversation: Context Saturation and Discourse-Marker Evidence
arXiv:2601.00181v3 Announce Type: replace Abstract: We address two persistent gaps in Emotion Recognition in Conversation: which modeling choices materially affect performance, and how recognition findings connect to interpretable discourse-level patterns. We study both through a systematic investigation on IEMOCAP with cross-dataset validation on MELD. For recognition, we run controlled ablations with 10 random seeds and paired significance tests with multiple-comparisons correction, yielding three findings. First, conversational context is the dominant factor, but performance saturates quick
This paper offers a systematic investigation into emotion recognition in conversation, building on current research in AI and natural language processing.
Improved emotion recognition in AI systems can significantly enhance human-computer interaction, customer service, and mental health applications, making AI more perceptive and adaptive.
The understanding of contextual factors and discourse-level patterns in emotion recognition is refined, leading to more robust and interpretable AI models.
- · AI developers
- · Customer service platforms
- · Mental health tech
- · NLP researchers
- · Platforms without advanced emotion recognition
AI models become more effective at understanding and responding to human emotions in speech.
This leads to more natural and empathetic human-AI interactions across various applications.
Increased emotional intelligence in AI could spark new ethical considerations regarding AI manipulation or misuse.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL