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，理解诸如``意大利的首都是什么？''之类的问题``周日在西雅图开放了哪些中餐馆？''，即使对于机器来说也很简单。 尽管自然语言句子有可能充满含糊的微妙，复杂和泛滥，但它们也可以简单，直截了当且清晰。 该项目通过在明确的意义上识别``易于理解''的问题来形式化这种直觉。人们不愿意将可靠且可预测的用户界面换成智能但不可靠的用户界面。 为了满足用户，自然语言界面（NLIS）不应经常误解他们的问题（如果有的话）。 因此，该研究项目具有三个组成部分。 首先，它引入了一个理论框架，用于通过正式定义健全性和完整性的属性，并确定可以构建声音和完整的NLIS的一类可说的自然语言问题，以分析NLI的可靠性。 其次，结果表明，该理论通过衡量语义上可触犯的问题的普遍性以及在实践中测量声音和完整的NLI的性能，具有实用性的导入。 最后，该项目将框架扩展到对话系统和越来越广泛的自然语言句子。该研究有可能重振对NLI的基础研究，并具有更广泛的社会影响，即使他们对计算机科学的了解如何，使普通人更容易获得强大的信息资源。