用强化学习让对话系统既完成任务又懂情绪。
Emotionally Intelligent Task-oriented Dialogue Systems: Architecture, Representation, and Optimisation
- 基于大模型构建统一架构，端到端优化任务与情绪响应。
- 结合短期情感奖励和长期任务成功奖励，提升系统表现。
- 适合想打造有温度的智能客服或助手的研究者和开发者。
面向任务的对话（ToD）系统旨在通过自然语言交互帮助用户达成特定目标。尽管大型语言模型（LLMs）显著提升了语言流畅性和上下文理解能力，但构建高效且富有情感智能的ToD系统仍是复杂挑战。有效的ToD系统需在固有的噪声和模糊对话环境中，同时优化任务成功率、情感理解与响应能力以及信息传达精度。本文研究了ToD系统的架构、表征、优化及情感因素设计。我们构建了一个包含自然语言用户模拟器和不完美自然语言理解模块的复杂评估环境，并提出LUSTER——一种基于大模型的统一任务导向对话系统，采用端到端强化学习，融合短期（用户情绪）与长期（任务成功）奖励。实验表明，结合大模型能力与结构化奖励建模，可实现更鲁棒且情绪敏感的对话系统，为下一代对话代理提供了可行路径。
原文摘要 · Abstract (English)
Task-oriented dialogue (ToD) systems are designed to help users achieve specific goals through natural language interaction. While recent advances in large language models (LLMs) have significantly improved linguistic fluency and contextual understanding, building effective and emotionally intelligent ToD systems remains a complex challenge. Effective ToD systems must optimise for task success, emotional understanding and responsiveness, and precise information conveyance, all within inherently noisy and ambiguous conversational environments. In this work, we investigate architectural, representational, optimisational as well as emotional considerations of ToD systems. We set up systems covering these design considerations with a challenging evaluation environment composed of a natural-language user simulator coupled with an imperfect natural language understanding module. We propose \textbf{LUSTER}, an \textbf{L}LM-based \textbf{U}nified \textbf{S}ystem for \textbf{T}ask-oriented dialogue with \textbf{E}nd-to-end \textbf{R}einforcement learning with both short-term (user sentiment) and long-term (task success) rewards. Our findings demonstrate that combining LLM capability with structured reward modelling leads to more resilient and emotionally responsive ToD systems, offering a practical path forward for next-generation conversational agents.
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