Papers with CEE
POP-CEE: Position-oriented Prompt-tuning Model for Causal Emotion Entailment (2024.findings-acl)
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| Challenge: | Existing methods for emotion analysis in conversations ignore the specific semantic associations between emotions and cause utterances. |
| Approach: | They propose a position-oriented prompt-tuning model to solve the CEE task in an end-to-end manner. |
| Outcome: | The proposed model achieves state-of-the-art performance on a benchmark dataset. |
TSAM: A Two-Stream Attention Model for Causal Emotion Entailment (2022.coling-1)
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| Challenge: | Existing studies on EAC focus on Emotion Recognition in Conversations (ERC), i.e., recognizing emotion labels of utterances. |
| Approach: | They propose a two-stream attention model to capture correlations between utterances in a global view and classify multiple utterrances synchronously to capture emotion and speaker information in parallel. |
| Outcome: | The proposed model outperforms baselines and achieves new State-Of-The-Art (SOTA) performance. |