Acquiring Domain-Specific Dialog Information from Task-Oriented Human-Human Interaction through an Unsupervised Learning
Acquiring Domain-Specific Dialog Information from Task-Oriented Human-Human Interaction through an Unsupervised Learning
复制标题
通过无监督学习从面向任务的人机交互中获取特定领域的对话信息
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
Alexander I. Rudnicky
中科院分区：
文献类型：
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作者：
A. Chotimongkol;Alexander I. Rudnicky
我们描述了一种方法，用于获取特定于域的对话知识配置一个面向任务的对话系统，使用人与人的交互数据。这个问题的关键方面是设计一个对话信息表示和学习方法，支持捕获域信息的域内对话。为了表示用于学习目的的对话，我们基于我们的表示，基于表单的对话结构表示，基于可观察的结构。我们表明，这种表示是足够的建模现象，经常发生在几个不同的面向任务的领域，包括信息访问和解决问题。为了最终减少人类注释工作的目标，我们研究了无监督学习技术在获取基于形式的表示的组件（即任务、子任务和概念）中的使用。这些技术包括基于互信息和Kullback-Liebler距离的统计词聚类，TextTiling，基于HMM的分割和平分K均值文档聚类。通过一些修改，使这些算法更适合于推断口语对话的结构，无监督学习算法显示出了希望。
We describe an approach for acquiring the domain-specific dialog knowledge required to configure a task-oriented dialog system that uses human-human interaction data. The key aspects of this problem are the design of a dialog information representation and a learning approach that supports capture of domain information from in-domain dialogs. To represent a dialog for a learning purpose, we based our representation, the form-based dialog structure representation, on an observable structure. We show that this representation is sufficient for modeling phenomena that occur regularly in several dissimilar task-oriented domains, including information-access and problem-solving. With the goal of ultimately reducing human annotation effort, we examine the use of unsupervised learning techniques in acquiring the components of the form-based representation (i.e. task, subtask, and concept). These techniques include statistical word clustering based on mutual information and Kullback-Liebler distance, TextTiling, HMM-based segmentation, and bisecting K-mean document clustering. With some modifications to make these algorithms more suitable for inferring the structure of a spoken dialog, the unsupervised learning algorithms show promise.