From Heart to Words: Generating Empathetic Responses via Integrated Figurative Language and Semantic Context Signals (2025)

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.

## introduction whether through a gentle idiom like "i've got your back" or a careful choice of phrasing, people often reach beyond straightforward language to convey empathetic support @xcite @xcite . in particular, employing figurative language (e.g., metaphors or idioms) is a prominent tool used to this effect @xcite , and is commonly understood to enhance emotional expression by making the abstract more vivid and relatable @xcite . consider the two statements: "i understand it's tough" (literal) and "i understand it feels like fighting an endless battle" (figurative). the latter conveys stronger emotional resonance, fostering deeper connection with those seeking support. despite the intuitive advantages of generating more rhetorically and contextually targeted empathy, existing empathy generation research predominantly focuses on understanding the emotions of the speaker (the individual sharing their struggles) ( @xcite , rather than on how the responder (the individual providing support) conveys empathy. while some studies, such as work by @xcite , explore structured response intents (e.g., agreeing or suggesting), these approaches align responses with emotions rather than leveraging deeper rhetorical tools. this highlights a gap in exploring how these more complex language phenomena can be used to effectively convey empathy, particularly when informed by the speaker's context. we address this gap by investigating how both rhetorical craft (focusing on figurative language) and semantic context (focusing on empathy cause) contribute to empathetic response generation in a specialized mental health setting. we summarize our contributions as: through this work, we aim to inspire further exploration of targeted, nuanced signals in empathetic communication. while our findings are drawn from a specialized domain, we hope that they pave the way for broader study in diverse support settings. ## related work the importance of effectively communicating empathy has been demonstrated across multiple domains @xcite @xcite . computational approaches have specifically sought to automate empathetic response generation by modeling speaker emotions and generating appropriate replies, often using the popular empatheticdialogues dataset (rashkin et al., 2019) which contains emotion-labeled conversations @xcite . beyond emotion recognition, researchers have studied how the causes behind a speaker's emotions can better contextualize responses. for example, @xcite and @xcite analyzed emotion causes in the empatheticdialogues dataset to identify triggers behind the speaker's emotions. similarly, @xcite integrated emotion cause recognition into large language models (llms) for empathetic response generation. while these works focus on understanding the triggers (e.g., "i failed an exam") behind the speaker's own emotions (e.g., sadness), our work introduces empathy causes, which identify specific parts of the speaker's text that evoke empathy from a responder in interpersonal communication (see figure 2 for an example and section 3.2 for further details). recent works have also started considering communication strategies to guide empathetic response generation. for instance, @xcite introduced a dataset combining emotion labels and response intents (such as agreeing or suggesting), and in follow-up work they demonstrated how these intents could guide the generation of emotionally supportive and empathetic responses @xcite . similarly, @xcite incorporated reinforcement learning for empathetic rewriting. while these communication strategies can help shape empathetic responses, they do not explore more complex rhetoric. recent work on empathetic storytelling @xcite shows how narrative style elements, such as tone and phrasing in the speaker's text, can influence perceived empathy, demonstrating the potential for leveraging rhetorical device for empathetic response generation. figurative language is a powerful such tool that enriches emotional expression @xcite . computational studies have shown that incorporating figurative language-specifically metaphors, idioms, and hyperbole-can improve predictions of both emotion @xcite and empathy @xcite . despite the clear value of figurative language to empathetic expression, as highlighted by these works, figurative language remains unexplored in empathetic response generation. building on the success of emotion cause annotations in improving response generation @xcite , we integrate figurative language and empathy cause annotations to address both the rhetorical and contextual aspects of empathetic response generation. we aim to create responses that are not only emotionally engaging but also well-aligned with the speaker's concerns. ## empathetic response generation using the new cause annotations and figurative language metadata, we sought to generate empathetic responses that meaningfully address the struggles expressed in speaker posts through both emotional engagement and contextual alignment. we study these factors both independently and in concert with one another. an overview of our full pipeline is presented in figure 3 . although gold empathy cause labels are used in this study to demonstrate proof of concept, a promising future direction (and one facilitated by the new cause labels) involves the automated detection of empathy cause. ## analysis of generated responses in this section, we analyze the generated responses to gain deeper insights into various dimensions of empathetic expression. ## conclusion in this study, we explored a novel approach to empathetic response generation by integrating figurative language and semantic context signals via manually annotated empathy causes. this integration meaningfully improves the response quality across emotional, contextual, and linguistic dimensions, as demonstrated by automated metrics: bleu improves by 7.6% (0.764 → 0.822), ppl decreases by 36.7% (14.182 → 8.980), and lexical diversity increases by 7.6% (d-1: 0.515 → 0.569, d-2: 0.751 → 0.814) from plain fine-tuning. human evaluations affirm that combined (fig + cause ) achieves the highest ratings for empathy (4.426, +24.2%), relevance (4.135, +13.9%), and fluency (4.189, +30.6%) out of 5. these findings, supported by our psycholinguistic analysis and exploration of the most supportive responses, underscore the synergy between rhetoric and contextual alignment when generating empathetic responses. overall, this study advances empathetic response generation by investigating this balance, moving beyond conventional approaches focused solely on understanding speaker's emotions. it also offers valuable insights into what makes a response truly supportive and engaging. our work provides a more holistic approach to empathetic communication, addressing underlying factors that drive emotionally and socially supportive interactions.