Papers › FATRER: Full-Attention Topic Regularizer for Accurate and Robust Conversational...
FATRER: Full-Attention Topic Regularizer for Accurate and Robust Conversational Emotion Recognition
Yuzhao Mao, Di Lu, Xiaojie Wang, Yang Zhang
This paper concentrates on the understanding of interlocutors' emotions evoked in conversational utterances. Previous studies in this literature mainly focus on more accurate emotional predictions, while ignoring model robustness when the local context is corrupted by adversarial attacks. To maintain robustness while ensuring accuracy, we propose an emotion recognizer augmented by a full-attention topic regularizer, which enables an emotion-related global view when modeling the local context in a conversation. A joint topic modeling strategy is introduced to implement regularization from both representation and loss perspectives. To avoid over-regularization, we drop the constraints on prior distributions that exist in traditional topic modeling and perform probabilistic approximations based entirely on attention alignment. Experiments show that our models obtain more favorable results than state-of-the-art models, and gain convincing robustness under three types of adversarial attacks.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Emotion Recognition in Conversation | IEMOCAP | FATRER | Accuracy | 69.69 | #22 of 59 | Archive leaderboard | report |
| Emotion Recognition in Conversation | IEMOCAP | FATRER | Weighted-F1 | 69.35 | #22 of 59 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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