Past, Present, and Future: Conversational Emotion Recognition through Structural Modeling of Psychological Knowledge (2021.findings-emnlp)
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| Challenge: | Conversational Emotion Recognition (CER) is a task to predict the emotion of an utterance in the context of a conversation. |
| Approach: | They propose a pSychological-Knowledge-Aware Interaction Graph to model the emotional state of an utterance in the context of a conversation. |
| Outcome: | The proposed method achieves state-of-the-art and competitive performance on four popular CER datasets. |
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| Challenge: | Existing studies focus on modeling emotion influences with utterance-level features, with little attention paid on phrase-level semantic connection between utterrances. |
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| Challenge: | EmoTransKG establishes connections and transformations between emotions across open-textual events. |
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| Challenge: | Emotion recognition in conversation (ERC) is essential for dialogue systems to identify the emotions expressed by speakers. |
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| Challenge: | Existing methods for emotion recognition in dialogues do not consider the content of the target utterance. |
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| Challenge: | During a conversation, a person’s emotions are influenced by the other speaker’s utterances and their own emotional state over the utterrances. |
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| Challenge: | Existing methods to analyze emotions in textual conversations are limited . emotion detection is challenging because humans rely on context and commonsense knowledge to express emotions . |
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