| Challenge: | Empathy is essential in human social interaction. |
| Approach: | They propose to annotate healthy empathy detection datasets IEMPATHIZE and TwittEmp with 8 empathy intent labels and perform joint training for the two tasks. |
| Outcome: | The proposed framework outperforms baselines on the two datasets. |
Similar Papers
E-CORE: Emotion Correlation Enhanced Empathetic Dialogue Generation (2023.emnlp-main)
Copied to clipboard
| 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. |
ECC: An Emotion-Cause Conversation Dataset for Empathy Response (2025.emnlp-main)
Copied to clipboard
| 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. |
Improving Empathetic Response Generation by Recognizing Emotion Cause in Conversations (2021.findings-emnlp)
Copied to clipboard
| 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. |
Multi-dimensional Evaluation of Empathetic Dialogue Responses (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Prior efforts to measure conversational empathy focus on expressed communicative intents, but ignore the fact that conversation is also a collaboration involving both speakers and listeners. |
| Approach: | They propose a multi-dimensional empathy evaluation framework to measure both expressed intents from the speaker’s perspective and perceived empathy from the listener’s viewpoint. |
| Outcome: | The proposed framework measures both expressed intents from the speaker’s perspective and perceived empathy from the listener’s viewpoint. |
EmpHi: Generating Empathetic Responses with Human-like Intents (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing empathetic dialogue models lack emotion-dependent response generation . elaine mccartney: "i'm sorry to hear that! " |
| Approach: | They propose a model to generate empathetic responses with human-consistent intents . they aim to address the bias of the empathic intent distribution between epd models and humans . |
| Outcome: | The proposed model outperforms state-of-the-art models in terms of empathy, relevance, and diversity on automatic and human evaluation. |
A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support (2020.emnlp-main)
Copied to clipboard
| Challenge: | Empathy measurement has predominantly occurred in synchronous, face-to-face settings, and may not translate to asynchronous, text-based contexts. |
| Approach: | They propose a computational approach to understanding how empathy is expressed in online mental health platforms. |
| Outcome: | The proposed model can identify empathic conversations and extract rationales from them. |
Enhancing Emotional Support Conversations: A Framework for Dynamic Knowledge Filtering and Persona Extraction (2025.coling-main)
Copied to clipboard
| Challenge: | Existing dialogue models struggle to interpret context accurately due to irrelevant or misclassified knowledge, limiting their effectiveness in real-world scenarios. |
| Approach: | They propose a framework that dynamically filters relevant commonsense knowledge and extracts personalized information to improve empathetic dialogue generation. |
| Outcome: | The proposed framework outperforms existing models in coherence, emotional understanding, and response relevance on the ESConv dataset. |
Distilling Knowledge for Empathy Detection (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Empathy is the link between self and others. |
| Approach: | They employ multi-task training with knowledge distillation to integrate knowledge from available resources to detect empathy from the natural language in different domains. |
| Outcome: | The proposed approach yields better results on an existing news-related empathy dataset compared to strong baselines. |
Modeling Empathetic Alignment in Conversation (2024.naacl-long)
Copied to clipboard
| Challenge: | Empathy requires perspective-taking and is not explicitly modelled in NLP . |
| Approach: | They propose a new approach to recognizing alignment in empathetic speech, grounded in Appraisal Theory, and use reddit to study emotional conversations to examine alignment. |
| Outcome: | The proposed approach can recognize appraisals and alignments in empathetic speech, and mental health professionals engage with substantially more emotional alignment. |
Empathy Identification Systems are not Accurately Accounting for Context (2023.eacl-main)
Copied to clipboard
| Challenge: | Empathy is a fundamental phenomenon that allows us to better communicate and relate with others. |
| Approach: | They propose a simple model that checks if an input utterance is similar to a small set of empathetic examples, but does not consider dialogue context. |
| Outcome: | The proposed model outperforms state-of-the-art models on benchmarks and empathetic rationale extraction benchmarks. |