Existing user simulators (USs) for task-oriented dialogue systems only model
user behaviour on semantic and natural language levels without considering the
user persona and emotions. Optimising dialogue systems with generic user
policies, which cannot model diverse user behaviour driven by different
emotional states, may result in a high drop-off rate when deployed in the real
world. Thus, we present EmoUS, a user simulator that learns to simulate user
emotions alongside user behaviour. EmoUS generates user emotions, semantic
actions, and natural language responses based on the user goal, the dialogue
history, and the user persona. By analysing what kind of system behaviour
elicits what kind of user emotions, we show that EmoUS can be used as a probe
to evaluate a variety of dialogue systems and in particular their effect on the
user's emotional state. Developing such methods is important in the age of
large language model chat-bots and rising ethical concerns.

EmoUS 是一种用户模拟器，它可以学习用户情绪并在用户目标、对话历史和用户角色的基础上生成用户情绪、语义动作和自然语言响应，可以作为探针评估各种对话系统及其对用户情绪的影响。

EmoUS: 任务导向对话中模拟用户情绪

EmoUS: Simulating User Emotions in Task-Oriented Dialogues

Predicting user behaviour on a website is a difficult task, which requires
the integration of multiple sources of information, such as geo-location, user
profile or web surfing history. In this paper we tackle the problem of
predicting the user intent, based on the queries that were used to access a
certain webpage. We make no additional assumptions, such as domain detection,
device used or location, and only use the word information embedded in the
given query. In order to build competitive classifiers, we label a small
fraction of the EDI query intent prediction dataset
\cite{edi-challenge-dataset}, which is used as ground truth. Then, using
various rule-based approaches, we automatically label the rest of the dataset,
train the classifiers and evaluate the quality of the automatic labeling on the
ground truth dataset. We used both recurrent and convolutional networks as the
models, while representing the words in the query with multiple embedding
methods.

本文采用多种嵌入方法以及循环神经网络和卷积神经网络等模型，通过基于查询的分类预测用户意图，从而实现预测用户在网站上的行为，可有效跟踪用户的意图并提供更好的用户体验。