面向情感支持对话生成的基于语义相似度的人设抽取
Persona Extraction Through Semantic Similarity for Emotional Support Conversation Generation
AI总结：
针对对话前难获取用户人设的问题，提出PESS框架，通过语义相似度设计的完整性与一致性损失从对话中推断人设，可有效提升情感支持对话回复的生成质量。
摘要：
在当今世界，通过对话系统提供情感支持正变得愈发重要，因为它能在诸多对话场景中为心理健康和社交互动提供支撑。已有研究表明，使用人设（persona）对生成共情且具有支持性的回复十分有效，但这些研究通常依赖预先提供的人设，而非在对话过程中推断人设。然而，在对话开始前并非总能获取用户人设。为解决这一难题，我们提出了PESS（基于语义相似度的人设抽取，Persona Extraction through Semantic Similarity）——一种可从对话中自动推断信息丰富且一致的人设的新型框架。我们基于语义相似度分数设计了完整性损失和一致性损失：完整性损失促使模型生成缺失的人设信息，一致性损失则引导模型区分一致与不一致的人设。实验结果表明，由PESS推断出的高质量人设信息能有效生成情感支持回复。
英文摘要：
Providing emotional support through dialogue systems is becoming increasingly important in today's world, as it can support both mental health and social interactions in many conversation scenarios. Previous works have shown that using persona is effective for generating empathetic and supportive responses. They have often relied on pre-provided persona rather than inferring them during conversations. However, it is not always possible to obtain a user persona before the conversation begins. To address this challenge, we propose PESS (Persona Extraction through Semantic Similarity), a novel framework that can automatically infer informative and consistent persona from dialogues. We devise completeness loss and consistency loss based on semantic similarity scores. The completeness loss encourages the model to generate missing persona information, and the consistency loss guides the model to distinguish between consistent and inconsistent persona. Our experimental results demonstrate that high-quality persona information inferred by PESS is effective in generating emotionally supportive responses.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
