CAREER: Logical Form Induction

近年来，人工智能(AI)系统的自然语言处理能力取得了显著进展。除了它们众多的商业应用外，这些进展表明，人工智能系统可能是加深我们对人类如何理解自然语言的理解的强大工具。使用它们实现这一目的的一个主要障碍是，虽然它们似乎很好地模拟了类比推理的某些方面，但它们模拟复杂逻辑推理的能力显示出很大的改进空间。该项目开发了一个框架，用于将复杂的逻辑推理能力集成到人工智能系统的组件中，使其能够进行类比推理。为了支持这一框架的开发，该项目建立了一个大型数据集，通过使用人工智能系统来确定哪些类型的逻辑关系对提高该系统自身的逻辑推理能力最有用，从而捕获三种语言句子之间的逻辑关系。通过与研究生和本科生课程相结合，该项目成为加强方案编制和统计素养以及通过实际应用程序培训加强数据收集和数据管理技能的工具。该框架通过对人工智能系统使用的数字表示的种类施加限制，将逻辑表示集成到人工智能系统中，这些数字表示用于基于一些自然语言输入进行推理。这些约束是根据从系统的自然语言数字表示到逻辑表示的映射来定义的。这种映射是从头开始学习的，本身受到以下限制：(A)正确预测一种语言的实际使用者的推论--如项目下收集的大规模数据集--以及(B)构成：某些语言的含义必须从其部分的含义中预测出来。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Artificial intelligence (AI) systems’ natural language processing capabilities have made remarkable strides in recent years. Beyond their numerous commercial applications, these advances suggest that AI systems might be powerful tools for deepening our understanding of how humans comprehend natural language. A major obstacle to using them for this purpose is that, while they seem to simulate certain aspects of reasoning by analogy quite well, their capacity to simulate complex logical reasoning shows much room for improvement. This project develops a framework for integrating complex logical reasoning capabilities into the components of AI systems that make their ability to reason by analogy possible. To support the development of this framework, the project builds a large dataset capturing the logical relationships among sentences in three languages by using AI systems to determine which kinds of logical relationships are most useful for improving that system’s own logical reasoning capabilities. Through integration with graduate and undergraduate curricula, the project serves as a vehicle to enhance programming and statistical literacy as well as data collection and data management skills through training with hands-on applications. The framework integrates logical representations into AI systems by imposing constraints on the sorts of numeric representations that those systems use to make inferences on the basis of some natural language input. These constraints are defined in terms of a mapping from the system’s numeric representations of natural language to logical representations. This mapping is learned from scratch and itself constrained (a) to correctly predict inferences that actual speakers of a language make – as captured by the large-scale datasets collected under the project – and (b) to be compositional: the meaning of some piece of language must be predictable from the meanings of its parts.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.