Integrating Language and Acting

计算机的下一个前沿是自动个人助理，它可以以直观和自然的方式与人互动。实现这一点的关键能力之一是强大的口语交流。然而，目前的自动自然语言理解技术都依赖于完整句子作为交流的核心单位的概念。这一假设对于文本理解应用程序来说是有意义的，但在口语应用程序中却非常不自然。人与人之间的口语交流是循序渐进的，使用句子片段，通过更正、详细说明和确认来实现有效的协调行动。这一小笔探索性研究补助金(SGER)探索了新的增量处理模式的可能性，这些模式重新定义了交流的基本交流单位，并开发了使用这些单位的新理解技术。这项工作的关键思想之一是，交际单位的定义应该是识别说话人的意图(而不是根据语言的结构属性来分析语言的传统观点)。这个项目正在探索开发新的计算模型的可行性，以逐步识别意图。探索性测试是使用TRIPS自然语言理解系统完成的，并比较了增量和非增量处理模型的性能。该SGER将为新一代自动化个人助理中更自然的人机界面奠定基础，新一代自动化个人助理将彻底改变计算机的使用方式，使计算机能够适应人类，而不是迫使人类适应计算机。

The next frontier for computers is the automated personal assistant that can interact with people in intuitive and natural terms. One of the key capabilities to enable this is robust spoken language communication. Current techniques for automated natural language understanding, however, all depend on the notion of a complete sentence as the core unit on communication. This assumption makes sense for text understanding applications, but is very unnatural in spoken language applications. Human-human spoken communication occurs incrementally, using sentence fragments, with corrections, elaborations, and confirmations to enable efficient coordinated action. This Small Grant for Exploratory Research (SGER) explores the possibility for new models of incremental processing that redefine the basic communicative unit of communication and develop new understanding techniques that use these units. One of the key ideas underlying this work is that communicative units should be defined in terms of identifying the speaker intentions (as opposed to the traditional view of analyzing language in terms of its structural properties). This project is exploring the feasibility of developing new computational models that identifying intentions incrementally. Exploratory testing is accomplished using the TRIPS natural language understanding system and comparing the performance of incremental vs non-incremental processing models.This SGER will lay the foundations for significantly more natural human-computer interfaces in a new generation of automated personal assistants that will revolutionized how computers are used, allowing computers to adapt to people rather than forcing humans to adapt to computers.