ITR: Semantically Tractable Questions: Theory and Implementation

ITR：语义上可处理的问题：理论与实施

• 批准号: 0312988
• 负责人: Oren Etzioni
• 金额: $ 39.5万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-08-15 至 2007-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0312988&HistoricalAwards=false
• 关键词: ITR; Semantically; Tractable; Questions; Theory

Understanding arbitrary natural language sentences is widely regarded as very challenging. Yet understanding questions such as ``What is the capital of Italy?'' or ``What Chinese restaurants are open on Sunday in Seattle?'' seems straightforward even for a machine. While natural language sentences have the potential to be subtle, complex, and rife with ambiguity, they can also be simple, straight forward, and clear. This project formalizes this intuition by identifying classes of questions that are ``easy to understand'' in a well defined sense. People are unwilling to trade reliable and predictable user interfaces for intelligent but unreliable ones. To satisfy users, Natural Language Interfaces (NLIs) should not be allowed to misinterpret their questions often, if at all. Consequently, this research project has three components. First, it introduces a theoretical framework for analyzing the reliability of an NLI by formally defining the properties of soundness and completeness and identifying a class of semantically tractable natural language questions for which sound and complete NLIs can be built. Second, it is shown that the theory has practical import by measuring the prevalence of semantically tractable questions and by measuring the performance of a sound and complete NLI in practice. Finally, the project extends the framework to dialog systems and to increasingly broad classes of natural language sentences.The research has the potential to reinvigorate basic research on NLIs, and to have the broader societal impact of making powerful information resources more readily available to ordinary people regardless of their knowledge of Computer Science.

理解任意的自然语言句子被广泛认为是非常具有挑战性的。然而，要理解“意大利的首都是什么？”这样的问题。或者‘西雅图周日有哪些中餐馆营业？’即使对于一台机器来说，这似乎也很简单。虽然自然语言句子可能是微妙、复杂和充满歧义的，但它们也可以简单、直截了当和清晰。这个项目通过确定明确定义的“容易理解”的问题类别来形式化这种直觉。人们不愿意用可靠和可预测的用户界面来换取智能但不可靠的用户界面。为了让用户满意，自然语言接口(NLIS)不应该被允许经常曲解他们的问题，如果有的话。因此，本研究项目由三个部分组成。首先，通过形式化地定义完备性和完备性的性质，识别一类语义易处理的自然语言问题，并为其建立健全和完整的自然语言信息系统，介绍了一个分析自然语言信息系统可靠性的理论框架。其次，通过测量语义易处理疑问句的普及率和一个完整的自然语言输入在实践中的表现，表明该理论具有实际意义。最后，该项目将该框架扩展到对话系统和越来越广泛的自然语言句子类别。这项研究有可能重振自然语言信息系统的基础研究，并产生更广泛的社会影响，使强大的信息资源更容易为普通人提供，而无论他们是否了解计算机科学。