强化学习在对话策略选择中的应用 在电子邮件口语对话系统中

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摘要翻译：
本文描述了一种新的方法，通过这种方法，口语对话系统可以从与用户交互的经验中学习选择最佳的对话策略。该方法基于强化学习和口语对话系统性能建模的结合。强化学习组件应用Q-learning(Watkins，1989)，而绩效建模组件应用PARADISE评估框架（Walker et al.，1997)来学习强化学习中使用的绩效函数（奖赏）。我们用一个口语对话系统ELVIS（EmaiL Voice Interactive system）来说明该方法，该系统支持通过电话访问电子邮件。我们在一个包含219个对话的语料库上进行了一系列实验，训练了一个最佳的对话策略。在这些对话中，人类用户与猫王通过电话进行了交互。然后我们在一个由18个对话组成的语料库上测试这个策略。我们表明，ELVIS可以学习优化它的策略选择，为代理主动，为阅读消息，为总结电子邮件文件夹。
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英文标题：
《An Application of Reinforcement Learning to Dialogue Strategy Selection
  in a Spoken Dialogue System for Email》
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作者：
M. A. Walker
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  This paper describes a novel method by which a spoken dialogue system can learn to choose an optimal dialogue strategy from its experience interacting with human users. The method is based on a combination of reinforcement learning and performance modeling of spoken dialogue systems. The reinforcement learning component applies Q-learning (Watkins, 1989), while the performance modeling component applies the PARADISE evaluation framework (Walker et al., 1997) to learn the performance function (reward) used in reinforcement learning. We illustrate the method with a spoken dialogue system named ELVIS (EmaiL Voice Interactive System), that supports access to email over the phone. We conduct a set of experiments for training an optimal dialogue strategy on a corpus of 219 dialogues in which human users interact with ELVIS over the phone. We then test that strategy on a corpus of 18 dialogues. We show that ELVIS can learn to optimize its strategy selection for agent initiative, for reading messages, and for summarizing email folders.
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PDF链接：
https://arxiv.org/pdf/1106.0241

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关键词：电子邮件 口语对话 Intelligence Presentation performance 系统 应用 进行 dialogue 策略