所有学习都有情感基础，面向任务的对话亦然
All Learning Has an Emotional Basis, So Does Task-Oriented Dialogue
AI总结：
本文提出一种结合词汇化表示与大语言模型的端到端框架，通过在线强化学习联合优化情感与任务成功，实验证明该框架在多样ToD环境中显著提升任务成功率与用户情感体验。
摘要：
任务导向对话（ToD）系统旨在通过自然语言交互帮助用户实现目标。除了任务成功之外，有效的ToD系统还必须在固有噪声和模糊的对话环境中维持积极的情感互动并准确传达信息。近期大语言模型（LLMs）的进展显著提升了对话流畅性和上下文理解能力。然而，尽管情感已被纳入现有的ToD系统，其整合通常局限于有限角色，限制了对整体对话行为和用户体验的影响。为解决这一问题，我们提出了一种新颖的端到端框架，该框架将完全词汇化的表示与大语言模型骨干相结合以提高语义准确性，并采用在线强化学习，结合短期情感奖励和长期任务成功奖励，以实现情感互动与任务成功的联合优化。我们进一步开发了一个具有挑战性的仿真环境，能够在多样化的ToD设置中进行系统性评估。实验表明，纳入粗略的情感信号总体上提高了任务成功率和情感评分。此外，使用我们提出的框架在整个对话系统中整合情感互动，在任务成功和用户情感体验方面均带来了更显著的改进。
英文摘要：
Task-oriented dialogue (ToD) systems aim to help users accomplish goals through natural language interaction. Beyond task success, effective ToD systems must also maintain positive emotional interaction and accurately convey information in inherently noisy and ambiguous conversational environments. Recent advances in large language models (LLMs) have substantially improved conversational fluency and contextual understanding. However, although emotion has been incorporated into existing ToD systems, its integration is typically confined to limited roles, restricting its influence on overall dialogue behaviour and user experience. To address this, we propose a novel end-to-end framework that combines fully lexicalised representations with an LLM backbone for improved semantic accuracy and employs online reinforcement learning with short-term emotional and long-term task-success rewards for joint optimisation of emotional interaction and task success. We further develop a challenging and realistic simulation environment that enables systematic evaluation across diverse ToD set-ups. Experiments show that incorporating coarse affective signals improves task success and sentiment rating in aggregate. Furthermore, integrating emotional interaction throughout the dialogue system using our proposed framework results in more pronounced improvements in both task success and user emotional experience.
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。
