Papers with emotion inference
FERNet: Fine-grained Extraction and Reasoning Network for Emotion Recognition in Dialogues (2020.aacl-main)
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| Challenge: | Existing methods for emotion recognition in dialogues do not consider the content of the target utterance. |
| Approach: | They propose to model historical utterances without considering the content of the target utterant . they propose to use a fine-grained reasoning network to generate target-specific historical . |
| Outcome: | The proposed method achieves competitive performance compared with previous methods. |
ECERC: Evidence-Cause Attention Network for Multi-Modal Emotion Recognition in Conversation (2025.acl-long)
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| Challenge: | Existing methods for multi-modal emotion recognition in isolated utterances do not capture emotional causes, including emotional contagion, influences from others, and self-referenced or externally introduced events. |
| Approach: | They propose a multi-modal conversational emotion recognition system that integrates emotional evidence with contextual causes through five stages. |
| Outcome: | The proposed method achieves competitive performance on two widely used benchmark datasets, IEMOCAP and MELD. |
No Innocence in Styling: Discovery of Privacy Protection Capabilities and Security Risks in Consumer Generative AI Writing Assistants (2026.acl-industry)
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Mohd. Farhan Israk Soumik, Syed Mhamudul Hasan, Wanniarachchi Kankanamge Malithi Mithsara, Ahmed Imteaj, Abdur R. Shahid
| Challenge: | a recent study examines the dual-use nature of platform-level text stylization. |
| Approach: | They examine the dual-use nature of platform-level text stylization by examining their implications for privacy and platform safety. |
| Outcome: | The proposed model reduces emotion inference accuracy, lowers profiling risk, and increases error rates in misinformation detection. |
Global-Local Modeling with Prompt-Based Knowledge Enhancement for Emotion Inference in Conversation (2023.findings-eacl)
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| Challenge: | Existing studies on emotion recognition focus on recognizing emotions through a speaker’s utterance, while research on emotion inference predicts emotions of addressees through previous utterations. |
| Approach: | They propose a global-local modeling method based on recurrent neural networks and pre-trained language models to do emotion inference in conversation. |
| Outcome: | The proposed method achieves state-of-the-art on three datasets. |
Multiple Knowledge-Enhanced Interactive Graph Network for Multimodal Conversational Emotion Recognition (2024.findings-emnlp)
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| Challenge: | Multimodal Emotion Recognition in Conversations models struggle due to lack of Common Sense Knowledge (CSK). |
| Approach: | They propose a multimodal approach to integrate multiple knowledge into the edge representations by integrating textual and visual CSK. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two popular datasets. |
Emotion Inference in Multi-Turn Conversations with Addressee-Aware Module and Ensemble Strategy (2021.emnlp-main)
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| Challenge: | Empirical studies on three different benchmark conversation datasets demonstrate the effectiveness of the proposed model over several strong baselines. |
| Approach: | They propose an addressee-aware module to automatically learn whether the participant keeps the historical emotional state or is affected by others in the next upcoming turn. |
| Outcome: | The proposed model can predict the participant's emotion in the next upcoming turn without knowing the participant’s response yet. |