@inproceedings{lee-etal-2025-heart,
title = "From Heart to Words: Generating Empathetic Responses via Integrated Figurative Language and Semantic Context Signals",
author = "Lee, Gyeongeun and
Wang, Zhu and
Ravi, Sathya N. and
Parde, Natalie",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.231/",
doi = "10.18653/v1/2025.findings-acl.231",
pages = "4490--4502",
ISBN = "979-8-89176-256-5",
abstract = "Although generically expressing empathy is straightforward, effectively conveying empathy in specialized settings presents nuanced challenges. We present a conceptually motivated investigation into the use of figurative language and causal semantic context to facilitate targeted empathetic response generation within a specific mental health support domain, studying how these factors may be leveraged to promote improved response quality. Our approach achieves a 7.6{\%} improvement in BLEU, a 36.7{\%} reduction in Perplexity, and a 7.6{\%} increase in lexical diversity (D-1 and D-2) compared to models without these signals, and human assessments show a 24.2{\%} increase in empathy ratings. These findings provide deeper insights into grounded empathy understanding and response generation, offering a foundation for future research in this area."
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<abstract>Although generically expressing empathy is straightforward, effectively conveying empathy in specialized settings presents nuanced challenges. We present a conceptually motivated investigation into the use of figurative language and causal semantic context to facilitate targeted empathetic response generation within a specific mental health support domain, studying how these factors may be leveraged to promote improved response quality. Our approach achieves a 7.6% improvement in BLEU, a 36.7% reduction in Perplexity, and a 7.6% increase in lexical diversity (D-1 and D-2) compared to models without these signals, and human assessments show a 24.2% increase in empathy ratings. These findings provide deeper insights into grounded empathy understanding and response generation, offering a foundation for future research in this area.</abstract>
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%0 Conference Proceedings
%T From Heart to Words: Generating Empathetic Responses via Integrated Figurative Language and Semantic Context Signals
%A Lee, Gyeongeun
%A Wang, Zhu
%A Ravi, Sathya N.
%A Parde, Natalie
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F lee-etal-2025-heart
%X Although generically expressing empathy is straightforward, effectively conveying empathy in specialized settings presents nuanced challenges. We present a conceptually motivated investigation into the use of figurative language and causal semantic context to facilitate targeted empathetic response generation within a specific mental health support domain, studying how these factors may be leveraged to promote improved response quality. Our approach achieves a 7.6% improvement in BLEU, a 36.7% reduction in Perplexity, and a 7.6% increase in lexical diversity (D-1 and D-2) compared to models without these signals, and human assessments show a 24.2% increase in empathy ratings. These findings provide deeper insights into grounded empathy understanding and response generation, offering a foundation for future research in this area.
%R 10.18653/v1/2025.findings-acl.231
%U https://aclanthology.org/2025.findings-acl.231/
%U https://doi.org/10.18653/v1/2025.findings-acl.231
%P 4490-4502
Markdown (Informal)
[From Heart to Words: Generating Empathetic Responses via Integrated Figurative Language and Semantic Context Signals](https://aclanthology.org/2025.findings-acl.231/) (Lee et al., Findings 2025)
ACL