CAREER: Logical Form Induction

职业：逻辑形式归纳

• 批准号: 2237175
• 负责人: Aaron White
• 金额: $ 49.79万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-15 至 2028-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2237175&HistoricalAwards=false
• 关键词: CAREER; Logical; Form; Induction

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.

近年来，人工智能（AI）系统的自然语言处理能力取得了显着的进步。除了众多的商业应用外，这些进步表明，AI系统可能是加深我们对人类全面自然语言的理解的强大工具。为此目的，将它们用于此目的的主要障碍是，尽管它们似乎很好地模拟了推理的某些方面，但它们模拟复杂的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，将复杂的逻辑推理能力集成到AI系统的组件中，以使其以类比的方式推理能力。为了支持该框架的开发，该项目通过使用AI系统来确定哪种逻辑关系对改善该系统自己的逻辑推理能力最有用，从而构建了一个大型数据集，以三种语言的句子之间的逻辑关系捕获句子之间的逻辑关系。通过与研究生和本科课程的集成，该项目可以通过动手应用程序培训来增强编程和统计素养以及数据收集和数据管理技能的工具。该框架将逻辑表示形式集成到AI系统中，通过对这些系统使用的数字表示形式施加约束，以某种自然语言输入进行推断。这些约束是根据从系统的数字表示自然语言到逻辑表示的映射来定义的。该映射是从头到头学习的，并且本身受到控制（a），以正确预测一种语言的实际说话者的信息 - 由项目下收集的大规模数据集所捕获 - 以及（b）要组成：某些语言的含义必须从其部分的含义中预测，这些奖项通过其奖项来反映NSF的范围及其范围的范围。 标准。