论文标题
迈向情绪感知的用户模拟器进行任务为导向的对话
Towards Emotion-Aware User Simulator for Task-Oriented Dialogue
论文作者
论文摘要
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，早期终止通常发生在令人失望的对话中。但是，现有现成的用户模拟器通常假设一个理想且合作的用户与真实用户有所不同，并且不可避免地会导致次优的对话策略。在本文中，我们为以任务为导向的对话提出了一个情感感知的用户仿真框架，该框架基于COCS情感模型，以更新用户情绪和驱动用户操作，以生成与真实用户更相似的模拟行为。我们提出了一个线性实现（源代码将很快发布。）易于理解和扩展，并在两个特定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绪模拟结果符合常识，并且对不同领域具有良好的多功能性。同时，我们的框架为我们提供了另一种观点，以了解基于强化学习的对话政策模型的改进过程。
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.
