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.

我们的研究表明，融合用户情感检测和面向任务对话模型能够相互受益，并且是一种值得考虑的替代方法。我们的方法通过扩展用户情感检测到 SimpleToD，一个端到端的面向任务对话系统，来实现无缝融合，并基于单一的语言模型来做到此事。通过在 EmoWOZ 基准上使用 GPT-2 和 Llama-2 评估我们的方法，结果显示用户情感检测和任务结果的整体性能有所提高。我们的研究还表明，用户情感可以为系统响应提供有用的上下文条件，可以通过利用这些情感进一步提高响应的共情性。