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。

情感检测与任务导向对话建模的统一方法

A Unified Approach to Emotion Detection and Task-Oriented Dialogue  Modeling

Task-oriented dialogue is often decomposed into three tasks: understanding
user input, deciding actions, and generating a response. While such
decomposition might suggest a dedicated model for each sub-task, we find a
simple, unified approach leads to state-of-the-art performance on the MultiWOZ
dataset. SimpleTOD is a simple approach to task-oriented dialogue that uses a
single, causal language model trained on all sub-tasks recast as a single
sequence prediction problem. This allows SimpleTOD to fully leverage transfer
learning from pre-trained, open domain, causal language models such as GPT-2.
SimpleTOD improves over the prior state-of-the-art in joint goal accuracy for
dialogue state tracking, and our analysis reveals robustness to noisy
annotations in this setting. SimpleTOD also improves the main metrics used to
evaluate action decisions and response generation in an end-to-end setting:
inform rate by 8.1 points, success rate by 9.7 points, and combined score by
7.2 points.

SimpleTOD 采用单个因果语言模型解决任务导向的对话，具有先进的性能，并且可以完全利用预训练、开放领域的因果语言模型进行转移学习。它提高了对话状态跟踪的联合目标准确性，改善了在端到端设置中评估行动决策和响应生成的主要指标。