ITR: Semantically Tractable Questions: Theory and Implementation

理解任意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？"或"在西雅图星期天有哪些中国餐馆？“即使对机器来说也很简单。 虽然自然语言句子有可能是微妙的，复杂的，充满歧义，但它们也可以是简单的，直接的，清晰的。 这个项目通过确定在定义良好的意义上“易于理解”的问题类别来正式化这种直觉。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，不应该允许自然语言接口（NLI）经常误解他们的问题。 因此，本研究项目有三个组成部分。 首先，它介绍了一个理论框架，通过正式定义的性质的健全性和完整性，并确定一类语义上听话的自然语言问题，健全和完整的NLI可以建立的可靠性分析的NLI。 其次，通过测量语义易处理问题的流行程度和测量一个健全完整的NLI在实践中的表现，表明该理论具有实际意义。 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类。该研究有可能重振NLI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所用，无论他们的计算机科学知识如何。

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.