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：在当前的基于文本的任务型对话系统中，用户情感检测往往被忽视或被视为独立任务，需要额外训练。与此相反，我们的研究表明，将情感检测与任务型对话建模无缝融合能带来相互增益，因此是一个值得考虑的新方案。我们的方法通过扩展信念状态追踪以纳入情感检测，对端到端任务型对话系统SimpleToD进行增强，并依托单一语言模型实现。我们在EmoWOZ基准（带情感标注的MultiWOZ版本）上使用GPT-2和Llama-2进行评估。结果表明，情感检测和任务性能普遍提升。我们的发现还表明，用户情感能为系统响应提供有用的上下文条件，并可进一步用于生成更具同理心的回复。