ECC: Synergizing Emotion, Cause and Commonsense for Empathetic Dialogue Generation (2025.coling-main)
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| Challenge: | Empathy improves human-machine dialogue systems by enhancing the user's experience. |
| Approach: | They propose a framework that leverages specialized encoders to capture the key features of emotion, cause, and commonsense and collaboratively models these through a Conditional Variational Auto-Encoder. |
| Outcome: | Empirical results show that the framework outperforms baseline models and offers a robust solution for empathetic dialogue generation. |
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| Challenge: | Existing empathy dialogue datasets focus on emotion labels while cause annotations are added post hoc. |
| Approach: | They propose an emotion-cause conversation dataset with 2.4K dialogues that can be scalable . they use a framework that utilizes knowledge and large language models to automatically generate dialogues . |
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Improving Empathetic Dialogue Generation by Dynamically Infusing Commonsense Knowledge (2023.findings-acl)
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| Challenge: | Existing work on generating empathetic responses by utilizing the speaker's emotion has not been successful. |
| Approach: | They propose an approach which incorporates an adaptive module for commonsense knowledge selection to ensure consistency between the generated empathetic responses and the speaker’s situation. |
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Empathetic Dialogue Generation via Sensitive Emotion Recognition and Sensible Knowledge Selection (2022.findings-emnlp)
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| Challenge: | Empathy is a key trait of everyday human conversations. |
| Approach: | They propose a serial encoding and Emotion-Knowledge interaction method for empathetic dialogue generation which is more sensitive to emotion dynamics in conversations. |
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EmpCRL: Controllable Empathetic Response Generation via In-Context Commonsense Reasoning and Reinforcement Learning (2024.lrec-main)
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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. |
| Approach: | They propose to use commonsense reasoning and reinforcement learning to generate empathetic response based on in-context commonsensing and contextual reasoning to broaden cognitive boundaries. |
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E-CORE: Emotion Correlation Enhanced Empathetic Dialogue Generation (2023.emnlp-main)
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| Challenge: | Empathy is a desirable human trait that improves the emotional perceptivity in emotion-bonding social activities. |
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Constructing Emotional Consensus and Utilizing Unpaired Data for Empathetic Dialogue Generation (2021.findings-emnlp)
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| Challenge: | Existing models for dialogue empathy focus on the emotion flow in one direction, from context to response. |
| Approach: | They propose a dual-generative model to construct emotional consensus and use unpaired data to produce pseudo paired empathetic samples. |
| Outcome: | The proposed model outperforms baseline models in producing coherent and empathetic responses. |
Improving Empathetic Response Generation by Recognizing Emotion Cause in Conversations (2021.findings-emnlp)
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| Challenge: | Existing approaches to empathetic response generation ignore the emotion cause . existing dialogue systems lack emotion understanding and empathy . |
| Approach: | They propose a framework that integrates emotion cause information into empathetic response generation by predicting context emotion labels and sequence of emotion cause-oriented labels. |
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Multi-Party Empathetic Dialogue Generation: A New Task for Dialog Systems (2022.acl-long)
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| Challenge: | Existing work on empathetic dialogues focused on the two-party scenario, but multi-party dialogues are pervasive in reality. |
| Approach: | They propose a multi-party empathetic dialogue generation task that uses a static-dynamic model to explore emotion and sensibility. |
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Non-Emotion-Centric Empathetic Dialogue Generation (2025.coling-main)
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| Challenge: | Empathy is a social psychology theory that enables individuals to comprehend each other's experiences and emotions, thereby fostering more intimate interpersonal relationships. |
| Approach: | They propose a framework for empathetic dialogue generation based on contrastive learning and context-sensitive entity and social commonsense that punishes responses with incorrect emotions and improves the quality of emotions. |
| Outcome: | The proposed framework improves the quality of empathetic generation and generates more diverse responses in comparison with the state-of-the-art baselines. |
From Multilingual Complexity to Emotional Clarity: Leveraging Commonsense to Unveil Emotions in Code-Mixed Dialogues (2023.emnlp-main)
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| Challenge: | Understanding emotions during conversation is a fundamental aspect of human communication. |
| Approach: | They propose an approach that integrates commonsense information with dialogue context to facilitate a deeper understanding of emotions. |
| Outcome: | The proposed approach improves ERC for code-mixed conversations by integrating commonsense with dialogue context. |