| Challenge: | a recent work on empathy prediction has underestimated the complexity of the phenomenon and lacks a shared corpus. authors present a novel annotation methodology which reliably captures empathy assessments by the writer of a statement using multi-item scales. |
| Approach: | They propose a method which captures empathy assessments by the writer of a statement using multi-item scales. |
| Outcome: | The proposed method distinguishes between multiple forms of empathy, empathic concern, and personal distress, as recognized throughout psychology. |
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A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support (2020.emnlp-main)
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| 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. |
EMPATH: An Ensemble Method for Automatic Fine-Grained Turn-Level Dialogue Empathy Evaluation with a Novel Emotional Distance Metric (2026.findings-acl)
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| Challenge: | Empathy evaluation metrics are lacking in the competitions, and classical dialogue evaluation metrics require further investigation. |
| Approach: | They propose a framework which combines fine-tuned models, large language models, classical dialogue evaluation metrics, and a novel metric. |
| Outcome: | The proposed framework improves on the WASSA 2024 benchmark and shows a statistically significant 8% improvement on the EX dataset. |
Towards Empathetic Open-domain Conversation Models: A New Benchmark and Dataset (P19-1)
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| Challenge: | EmpatheticDialogues dataset provides a benchmark for empathetic dialogue generation . human evaluators perceive dialogue models as more epathetic . |
| Approach: | They propose a benchmark for empathetic dialogue generation from a dataset of 25k conversations grounded in emotional situations. |
| Outcome: | The proposed benchmarks show that existing models are perceived to be more empathetic by human evaluators compared to models trained on large-scale Internet conversations. |
Can Machines Resonate with Humans? Evaluating the Emotional and Empathic Comprehension of LMs (2024.findings-emnlp)
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| Challenge: | Empathy plays a pivotal role in fostering prosocial behavior, often triggered by the sharing of personal experiences through narratives. |
| Approach: | They propose to use contrastive learning with masked LMs and supervised fine-tuning with large language models to improve empathy understanding in NLP models. |
| Outcome: | The proposed methods show that there is low agreement among annotators and that cultural differences are a factor in their interpretation of empathy. |
Learning Word Ratings for Empathy and Distress from Document-Level User Responses (2020.lrec-1)
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| Challenge: | Emotion analysis of text is increasing in popularity in NLP, however, manually creating lexica for psychological constructs such as empathy has proven difficult. |
| Approach: | They compare different approaches to learning word ratings from higher-level supervision and use a Mixed-Level Feed Forward Network to create the first-ever empathy lexicon. |
| Outcome: | The proposed model automatically creates empathy word ratings from document-level ratings. |
Modeling Empathic Similarity in Personal Narratives (2023.emnlp-main)
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| Challenge: | EmpathicStories is a dataset of 1,500 personal stories annotated with empathic similarity features and 2,000 pairs of stories annnotated by empathism. |
| Approach: | They propose a task to identify similarity in personal stories based on empathic resonance . they use a dataset of 1,500 personal stories annotated with empathism features . |
| Outcome: | The proposed model outperforms semantic similarity models on correlation and retrieval metrics. |
EmpathicStories++: A Multimodal Dataset for Empathy Towards Personal Experiences (2024.findings-acl)
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Jocelyn Shen, Yubin Kim, Mohit Hulse, Wazeer Zulfikar, Sharifa Alghowinem, Cynthia Breazeal, Hae Park
| Challenge: | Existing datasets for empathy modeling are limited in the ways they are not captured in the wild. |
| Approach: | They propose a multimodal dataset for empathy during personal experience sharing that contains 53 hours of video, audio, and text data of 41 participants. |
| Outcome: | The EmpathicStories++ dataset contains 53 hours of video, audio, and text data of 41 participants sharing vulnerable experiences and reading empathically resonant stories with an AI agent. |
LLM-GEm: Large Language Model-Guided Prediction of People’s Empathy Levels towards Newspaper Article (2024.findings-eacl)
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| Challenge: | Empathy is a key component of human-to-human interactions, and is often overlooked due to the inherent noise in crowdsourced annotations. |
| Approach: | They propose a large language model-guided empathy prediction system that rectifies annotation errors based on defined annotation selection threshold and makes annotations reliable for conventional empathy prediction models. |
| Outcome: | The proposed system rectifies annotation errors based on defined selection threshold and makes the annotations reliable for conventional empathy prediction models, e.g., BERT-based pre-trained language models. |
Supporting Cognitive and Emotional Empathic Writing of Students (2021.acl-long)
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| Challenge: | Empathy skills are an elementary skill in society for daily interaction and professional communication and are therefore elementary for educational curricula. |
| Approach: | They propose an annotation approach to capture emotional and cognitive empathy in student-written peer reviews on business models in germany. |
| Outcome: | The proposed annotation scheme guides annotators to a substantial to moderate agreement with the model and shows that it is effective. |
Empathy Applicability Modeling for General Health Queries (2026.findings-acl)
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| Challenge: | Existing NLP frameworks focus on reactively labeling empathy in doctors’ responses but offer limited support for anticipatory modeling of empathy needs, especially in general health queries. |
| Approach: | They propose an Empathy Applicability Framework that classifies patient queries in terms of the applicability of emotional reactions and interpretations based on clinical, contextual, and linguistic cues. |
| Outcome: | The Empathy Applicability Framework outperforms heuristic and zero-shot LLMs in the clinical setting. |