Papers with conversational emotion recognition
Beyond Verbal Cues: Emotional Contagion Graph Network for Causal Emotion Entailment (2025.findings-acl)
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| Challenge: | Recent studies have focused on identifying the causes of emotions by understanding verbal contextual utterances, but this study often lacks recognizing the underlying emotional stimuli present in these utterrances. |
| Approach: | They propose an Emotional Contagion Graph Network that simulates the impact of non-verbal emotional cues on the counterpart’s emotions. |
| Outcome: | The proposed model is compared with state-of-the-art models on a benchmark dataset and the results are encouraging. |
Beyond Linguistic Cues: Fine-grained Conversational Emotion Recognition via Belief-Desire Modelling (2024.lrec-main)
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| Challenge: | Emotion recognition in conversation (ERC) is essential for dialogue systems to identify the emotions expressed by speakers. |
| Approach: | They propose a method that incorporates both belief and desire to accurately identify emotions by extracting emotion-eliciting events from utterances and construct graphs that represent beliefs and desires in conversations. |
| Outcome: | The proposed model outperforms existing models on four popular ERC datasets and validates its performance with multiple state-of-the-art models. |
EmoTransKG: An Innovative Emotion Knowledge Graph to Reveal Emotion Transformation (2024.findings-acl)
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| Challenge: | EmoTransKG establishes connections and transformations between emotions across open-textual events. |
| Approach: | They propose an Emotion Knowledge Graph that establishes connections and transformations between emotions across diverse open-textual events. |
| Outcome: | The proposed model integrates with existing conversational emotion recognition models to improve the quality and effectiveness of EmoTransKG. |
JoPR: Joint Emotion Perception and Reasoning for Conversational Emotion Recognition (2026.acl-long)
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| Challenge: | Existing methods for ERC lack human-like emotion reasoning and discrimination between similar emotions. |
| Approach: | They propose a multi-dimension curriculum with long CoT fine-tuning to clone human-like emotion reasoning for conversational emotion recognition. |
| Outcome: | The proposed model outperforms existing methods on three widely used datasets and shows that it is more intuitive and more accurate. |