Parameter estimation for agenda-based user simulation
Parameter estimation for agenda-based user simulation
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发表时间：
2010-09
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通讯作者：
Simon Keizer;Milica Gasic;Filip Jurcícek;François Mairesse;Blaise Thomson;Kai Yu;S. Young
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作者：
Simon Keizer;Milica Gasic;Filip Jurcícek;François Mairesse;Blaise Thomson;Kai Yu;S. Young
本文提出了一种基于MATLAB的用户模拟器，它已被扩展到可训练的真实的数据，目的是更紧密地模拟复杂的理性行为所表现出的真实的用户。可训练部分由一组随机决策点形成，这些决策点可能在接收系统动作并以用户动作进行响应的过程中遇到。提出了一种基于样本的方法，用于使用真实的用户数据来估计控制这些决策的参数。评估结果给出了在统计生成的用户行为和不同的模拟器训练的政策的质量。与手工制作的模拟器相比，经过训练的系统提供了更好的语料库数据拟合，评估表明，这种更好的拟合应该会提高对话性能。
This paper presents an agenda-based user simulator which has been extended to be trainable on real data with the aim of more closely modelling the complex rational behaviour exhibited by real users. The trainable part is formed by a set of random decision points that may be encountered during the process of receiving a system act and responding with a user act. A sample-based method is presented for using real user data to estimate the parameters that control these decisions. Evaluation results are given both in terms of statistics of generated user behaviour and the quality of policies trained with different simulators. Compared to a handcrafted simulator, the trained system provides a much better fit to corpus data and evaluations suggest that this better fit should result in improved dialogue performance.