面向任务的会话用户模拟器的研究
Towards Emotion-Aware User Simulator for Task-Oriented Dialogue
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。此外，提前终止经常发生在令人失望的谈话中。然而，现有的现成的用户模拟器通常假设一个理想的和合作的用户，这是从一个真实的用户有些不同，并不可避免地导致一个次优的对话策略。本文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，通过更新用户情感和驱动用户动作，生成更接近真实用户的模拟行为。我们提出了一个线性实现（源代码将很快发布。它易于理解和扩展，并在两个特定领域数据集上进行评估。实验结果表明，我们提出的框架的情感模拟结果符合常识，并具有良好的通用性，为不同的领域。同时，该框架为我们理解基于强化学习的对话策略模型的改进过程提供了另一个视角。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
