A Taxonomy of Empathetic Response Intents in Human Social Conversations (2020.coling-main)
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| Challenge: | Open-domain conversational agents or chatbots are becoming increasingly popular in the natural language processing community. |
| Approach: | They aim to combine dialogue act/intent modelling and neural response generation to produce a large-scale taxonomy for empathetic response intents. |
| Outcome: | The proposed method improves the response quality of chatbots and makes them more controllable and interpretable. |
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| Challenge: | Existing empathetic dialogue models lack emotion-dependent response generation . elaine mccartney: "i'm sorry to hear that! " |
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| Challenge: | Existing approaches focus on acquiring affective and cognitive knowledge from text, but neglect the unique personality traits of individuals and the inherently multimodal nature of human face-to-face conversation. |
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| Challenge: | Existing empathetic dialogue models only consider the affective aspect of empathy, which limits the capability of emotional response generation. |
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| Challenge: | EmpathyEar is an open-source, avatar-based multimodal empathetic chatbot . currently, ERG systems rely on text, sound, and vision . |
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| Challenge: | Existing studies lack the perception of fine-grained dialogue emotion propagation, and have limitations in reasoning about the intentions of users on cognition, which affect the quality of empathetic response. |
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EmpDG: Multi-resolution Interactive Empathetic Dialogue Generation (2020.coling-main)
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| Challenge: | Existing work on empathetic dialogue generation fails to capture the nuances of human emotion and consider the potential of user feedback. |
| Approach: | They propose a multi-resolution adversarial model - EmpDG - to generate more empathetic responses by exploiting both coarse-grained dialogue-level and fine-grounded token-level emotions. |
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