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

Instructions-tuned Large Language Models (LLMs) gained recently huge
popularity thanks to their ability to interact with users through conversation.
In this work we aim to evaluate their ability to complete multi-turn tasks and
interact with external databases in the context of established task-oriented
dialogue benchmarks. We show that for explicit belief state tracking, LLMs
underperform compared to specialized task-specific models. Nevertheless, they
show ability to guide the dialogue to successful ending if given correct slot
values. Furthermore this ability improves with access to true belief state
distribution or in-domain examples.

本研究旨在研究大型语言模型在多轮任务和与外部数据库交互方面的能力，发现在显式信仰状态跟踪方面，它们表现不如专门的任务特定模型，但是如果给出正确的插槽值，它们表现出将对话引导到成功结局的能力，并且在有真实信仰状态分布或域内示例的情况下，这种能力得到了改进。

LLM 对于面向任务的对话系统是否足够？

Are LLMs All You Need for Task-Oriented Dialogue?

This paper presents a hybrid dialog state tracker that combines a rule based
and a machine learning based approach to belief state tracking. Therefore, we
call it a hybrid tracker. The machine learning in our tracker is realized by a
Long Short Term Memory (LSTM) network. To our knowledge, our hybrid tracker
sets a new state-of-the-art result for the Dialog State Tracking Challenge
(DSTC) 2 dataset when the system uses only live SLU as its input.

本文介绍了一个混合对话状态跟踪器，它结合了基于规则和基于机器学习的方法来进行信念状态跟踪，其中机器学习部分采用了 LSTM 网络，并在使用实时 SLU 作为输入的情况下，达到了 DSTC2 数据集的新的最先进结果。