Papers with utterance-level emotion recognition
HiGRU: Hierarchical Gated Recurrent Units for Utterance-Level Emotion Recognition (N19-1)
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| Challenge: | Using textual features, our proposed HiGRU models achieve at least 8.7%, 7.5%, 6.0% improvement over the state-of-the-art methods on each dataset. |
| Approach: | They propose a hierarchical gated recurrent unit framework to model word-level inputs and an upper-level GRU to capture contexts of utterance-level embeddings. |
| Outcome: | The proposed framework achieves 8.7%, 7.5%, 6.0% improvement over state-of-the-art methods on three datasets. |
A Facial Expression-Aware Multimodal Multi-task Learning Framework for Emotion Recognition in Multi-party Conversations (2023.acl-long)
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| Challenge: | Recent studies have shown the importance of visual information in multi-party conversations due to the complexity of visual scenes. |
| Approach: | They propose a framework to extract face sequences as visual features from a real speaker's utterance and a pipeline method to extract the face sequence. |
| Outcome: | The proposed framework extracts face sequences of the real speaker of each utterance and improves emotion prediction on the MELD dataset. |