RI: Implicit Learning in Spoken Language Interfaces

口语系统很难建立，部分原因是它们结合了许多不同的知识来源，每个来源都需要适应特定领域和应用的需求。对话中使用的学习方法通常(但不总是)依赖于标签语料库的存在，从该语料库可以估计模型参数；不幸的是，这需要人工创建标签或注释。该项目正在开发一种新的方法，内隐学习，它通过利用对话中出现的自然模式来生成学习实例，以解决当前的缺点。该项目从三个角度来解决这个问题：1)了解内隐学习提供的杠杆，特别是信息的质量、数量和学习机会的分布如何影响其效率。2)确定可获得学习机会的条件，特别是根据用户的成本与知识的收益来计算特定干预措施的效用。3)通过发现，特别是通过对过去交互的回顾分析，增加有用的模式集。该项目使用工作对话系统进行实验并收集经验数据进行分析。该项目中创建的技术和分析应适用于各种交互系统的设计，并允许它们纳入自然的非侵入性学习组件。它们的价值在于减少了对学习的专家监督的需要，以及相应地随着时间的推移演变行为的能力。因此，它们构成了强大情报的关键要素。

Spoken language systems are difficult to build in part because they combine many diverse sources of knowledge, each of which needs to fit the needs of a particular domain and application. Learning approaches used in dialogue often (though not always) rely on the existence of a labeled corpus from which models parameters can be estimated; unfortunately this requires human effort to create labels or annotations.This project is developing a new approach, implicit learning, that addresses current shortcomings by leveraging natural patterns that occur in conversation to generate learning instances.The project is approaching the problem from three perspectives: 1) Understanding the leverage provided by implicit learning, specifically how the quality of information, its quantity and the distribution of learning opportunities affects its efficiency. 2) Determining the conditions under which learning opportunities can be elicited, specifically computing the utility of given interventions in terms of cost to the user versus gain in knowledge. 3) Augmenting the set of useful patterns through discovery, specifically through the retrospective analysis of past interactions. The project is using working dialog systems to conduct experiments and gather empirical data for analysis.The techniques and analyses created in this project should be applicable to the design of a wide variety of interactive systems and allow them to incorporate a natural non-obtrusive learning component. Their value lies in the reduction of the need for expert supervision of learning and the corresponding ability to evolve behavior over time. As such they constitute a key element of robust intelligence.