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，或通常被视为独立任务，需要额外训练。相比之下，我们的研究表明，无缝统一ED与TOD建模能带来相互增益，因此是一种值得考虑的替代方案。我们的方法通过扩展信念状态跟踪以包含ED，基于单一语言模型，对端到端TOD系统SimpleToD进行增强。我们在EmoWOZ基准（一个标注了情感的MultiWOZ版本）上使用GPT-2和Llama-2评估了我们的方法。结果显示，ED和任务结果在性能上普遍提升。我们的发现还表明，用户情感为系统响应提供了有用的上下文条件，并可被用于在共情方面进一步优化响应。