Papers with Emotion
A Co-Attention Neural Network Model for Emotion Cause Analysis with Emotional Context Awareness (D18-1)
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| Challenge: | Existing methods ignore the contexts around the emotion word which can provide an emotion cause clue. |
| Approach: | They propose a co-attention neural network model for emotion cause analysis with emotional context awareness. |
| Outcome: | The proposed model outperforms the state-of-the-art methods. |
MELD-ST: An Emotion-aware Speech Translation Dataset (2024.findings-acl)
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Sirou Chen, Sakiko Yahata, Shuichiro Shimizu, Zhengdong Yang, Yihang Li, Chenhui Chu, Sadao Kurohashi
| Challenge: | Emotion plays a crucial role in human conversation. |
| Approach: | They present a MELD-ST dataset for the emotion-aware speech translation task . they show that fine-tuning with emotion labels can enhance translation performance . |
| Outcome: | The proposed dataset shows that fine tuning with emotion labels can improve translation performance in some settings. |
Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction (2022.coling-1)
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| Challenge: | Existing methods for ECPE fail to model specific features and interactive features in between, or suffer from inconsistency of label prediction. |
| Approach: | They propose to align ECPE with a feature-task alignment mechanism to model emotion-&cause-specific features and the shared interactive feature. |
| Outcome: | The proposed model outperforms existing systems on all ECA subtasks. |
PRISMA: Preference-Reinforced Self-Training Approach for Interpretable Emotionally Intelligent Negotiation Dialogues (2026.acl-long)
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| Challenge: | Emotion plays a pivotal role in shaping negotiation outcomes, influencing trust, cooperation, and long-term relationships. |
| Approach: | They propose an Emotion-aware Negotiation Strategy-informed Chain-of-Thought reasoning mechanism which mimics human negotiation by perceiving, understanding, using, and managing emotions. |
| Outcome: | The proposed system generates interpretable emotions and improves negotiation effectiveness on job interviews and resource allocation datasets. |