The performance of a task-completion dialogue agent usually affects the user experience: when the conversation system yields an unreasonable response, users may feel dissatisfied. Besides, early termination often occurs in disappointing conversations. However, existing off-the-shelf user simulators generally assume an ideal and cooperative user, which is somewhat different from a real user, and inevitably lead to a sub-optimal dialogue policy. In this paper, we propose an emotion-aware user simulation framework for task-oriented dialogue, which is based on the OCC emotion model to update user emotions and drive user actions, to generate simulated behaviors that more similar to real users. We present a linear implementation (The source code will be released soon.) that is easy to understand and extend, and evaluate it on two domain-specific datasets. The experimental results show that the emotional simulation results of our proposed framework conform to common sense and have good versatility for different domains. Meanwhile, our framework provides us with another perspective to understand the improvement process of the dialogue policy model based on reinforcement learning.

中文翻译：

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面向面向任务的对话的情感感知用户模拟器

任务完成对话代理的性能通常会影响用户体验：当对话系统产生不合理的响应时，用户可能会感到不满意。此外，提前终止经常发生在令人失望的谈话中。然而，现有的现成用户模拟器通常假设一个理想的合作用户，这与真实用户有些不同，并且不可避免地导致次优对话策略。在本文中，我们提出了一种面向任务对话的情感感知用户模拟框架，该框架基于OCC情感模型来更新用户情感并驱动用户动作，以生成更接近真实用户的模拟行为。我们提出了一个易于理解和扩展的线性实现（源代码即将发布），并在两个特定于领域的数据集上对其进行评估。实验结果表明，我们提出的框架的情感模拟结果符合常识，对不同领域具有良好的通用性。同时，我们的框架为我们提供了另一个视角来理解基于强化学习的对话策略模型的改进过程。