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.
Outcome: The proposed framework improves empathetic response generation by incorporating emotion cause information into the model.

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Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes (2021.emnlp-main)

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Challenge: Empathy is a complex cognitive ability based on the reasoning of others’ affective states.
Approach: They propose a method to infer emotion cause words from utterances without a word-level label and a novel method to make dialogue models focus on targeted words in the input during generation.
Outcome: The proposed method improves multiple best-performing dialogue agents on generating more focused empathetic responses in terms of automatic and human evaluation.
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.
Outcome: The proposed method outperforms baseline evaluations on the utterance-level annotated EMPATHETICDIALOGUES.
Wish I Can Feel What You Feel: A Neural Approach for Empathetic Response Generation (2022.findings-emnlp)

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Challenge: Existing approaches focus on a single factor that affects empathy but in practice it is complex and dynamic.
Approach: They propose a method which integrates emotion cause, knowledge graph, and communication mechanism for empathetic response generation.
Outcome: The proposed method generates more informative and empathetic responses on a large-scale dataset.
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.
Approach: They propose a framework that integrates emotion correlation learning, utilization, and supervising.
Outcome: The proposed framework improves empathetic perception and expression on a humanized dialogue dataset.
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.
Outcome: The proposed model outperforms state-of-the-art models in automatic and human evaluation.
Modelling Context Emotions using Multi-task Learning for Emotion Controlled Dialog Generation (2021.eacl-main)

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Challenge: Recent research has tackled this task using neural generative methods by augmenting emotion classes with the input sequences.
Approach: They propose to use a self-attention based encoder and a decoder with dot product attention mechanism to generate a viable response with a specified emotion.
Outcome: The proposed model outperforms baselines on automatic evaluation measures such as F1 and BLEU scores, thus resulting in more fluent and adequate responses.
ECC: An Emotion-Cause Conversation Dataset for Empathy Response (2025.emnlp-main)

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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 .
Outcome: The proposed dataset can achieve comparable or even superior performance to existing empathy dialogue datasets.
From Traits to Empathy: Personality-Aware Multimodal Empathetic Response Generation (2025.coling-main)

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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.
Approach: They propose a multimodal dialogue system that generates empathetic responses from a perspective that considers the personality traits of users.
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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.
Exploiting Emotion-Semantic Correlations for Empathetic Response Generation (2023.findings-emnlp)

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Challenge: Empathetic response generation aims to generate empathetic responses by understanding the speaker’s emotional feelings from the language of dialogue.
Approach: They propose a dynamical Emotion-Semantic Correlation Model (ESCM) which constructs dynamic emotion-semantics through the interaction of context and emotions.
Outcome: The proposed model understands emotions more accurately and expresses fluent and informative empathetic responses.

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