From Heart to Words: Generating Empathetic Responses via Integrated Figurative Language and Semantic Context Signals

ACL ARR 2025 February Submission4848 Authors

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.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: mental health support, empathetic response generation, figurative language, cause-aware text generation

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Data resources, Data analysis

Languages Studied: English

Submission Number: 4848