Papers › TSAM: A Two-Stream Attention Model for Causal Emotion Entailment
TSAM: A Two-Stream Attention Model for Causal Emotion Entailment
Duzhen Zhang, Zhen Yang, Fandong Meng, Xiuyi Chen, Jie zhou
Causal Emotion Entailment (CEE) aims to discover the potential causes behind an emotion in a conversational utterance. Previous works formalize CEE as independent utterance pair classification problems, with emotion and speaker information neglected. From a new perspective, this paper considers CEE in a joint framework. We classify multiple utterances synchronously to capture the correlations between utterances in a global view and propose a Two-Stream Attention Model (TSAM) to effectively model the speaker's emotional influences in the conversational history. Specifically, the TSAM comprises three modules: Emotion Attention Network (EAN), Speaker Attention Network (SAN), and interaction module. The EAN and SAN incorporate emotion and speaker information in parallel, and the subsequent interaction module effectively interchanges relevant information between the EAN and SAN via a mutual BiAffine transformation. Extensive experimental results demonstrate that our model achieves new State-Of-The-Art (SOTA) performance and outperforms baselines remarkably.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Causal Emotion Entailment | RECCON | EAN | Macro F1 | 80.24 | #4 of 9 | Archive leaderboard | report |
| Causal Emotion Entailment | RECCON | EAN | Neg. F1 | 90.48 | #4 of 9 | Archive leaderboard | report |
| Causal Emotion Entailment | RECCON | EAN | Pos. F1 | 70.00 | #4 of 9 | Archive leaderboard | report |
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