In current text-based task-oriented dialogue (TOD) systems, user emotion detection (ED) is often overlooked or is typically treated as a separate and independent task, requiring additional training. In contrast, our work demonstrates that seamlessly unifying ED and TOD modeling brings about mutual benefits, and is therefore an alternative to be considered. Our method consists in augmenting SimpleToD, an end-to-end TOD system, by extending belief state tracking to include ED, relying on a single language model. We evaluate our approach using GPT-2 and Llama-2 on the EmoWOZ benchmark, a version of MultiWOZ annotated with emotions. Our results reveal a general increase in performance for ED and task results. Our findings also indicate that user emotions provide useful contextual conditioning for system responses, and can be leveraged to further refine responses in terms of empathy.

翻译：在当前基于文本的任务导向对话（TOD）系统中，用户情感检测（ED）常被忽视或被当作独立任务处理，需要额外训练。相较之下，我们的研究表明，将ED与TOD建模无缝统一能带来双向增益，因此值得作为备选方案。本方法通过扩展信念状态追踪以纳入ED，并在单一语言模型基础上增强端到端TOD系统SimpleToD。我们使用GPT-2和Llama-2在EmoWOZ基准（带情感标注的MultiWOZ变体）上评估所提方法。实验结果显示，ED与任务性能均普遍提升。研究还表明，用户情感为系统响应提供了有效的上下文条件，可进一步用于优化响应的共情表达。