ESCP: Enhancing Emotion Recognition in Conversation with Speech and Contextual Prefixes (2024.lrec-main)
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| Challenge: | Emotion Recognition in Conversation (ERC) aims to analyze the speaker’s emotional state in a conversation. |
| Approach: | They propose to combine a directed acyclic graph and contextual prefixes to model historical utterances in a conversation and incorporate a contextual prefixed containing sentiment and semantics of historical . |
| Outcome: | The proposed model achieves state-of-the-art (SOTA) performance on several public benchmarks. |
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| Challenge: | Emotion recognition in conversations (ERC) is a task that aims to recognize the emotion of each utterance in conversations. |
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| Challenge: | Empirical evidence shows that a good representation of conversation context significantly contributes to the model performance. |
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| Challenge: | Current technologies in ERC are limited due to the need for future information. |
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| Challenge: | Existing approaches to Emotion Recognition in conversation (ERC) focus on modeling speaker dynamics within dialogues. |
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