Collaborative Research: The MegaAttitude Project: Investigating selection and polysemy at the scale of the lexicon

合作研究：MegaAttitude 项目：在词典范围内调查选择和多义现象

• 批准号: 1748969
• 负责人: Aaron White
• 金额: $ 28.43万
• 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: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1748969&HistoricalAwards=false
• 关键词: Collaborative; Research; MegaAttitude; Project; Investigating

This project addresses how humans draw complex inferences from the thousands of English predicates that combine with subordinate clauses -- "think", "know", "say", "tell", "remember", "forget", etc. -- when the structural characteristics of the clauses they combine with vary. For example, the sentence "John forgot that he bought milk" is similar to the sentence "John forgot to buy milk"; but from the first sentence, a listener infers that John bought milk, while from the second, a listener infers that he didn't. This inference pattern is only one among many such patterns in English; yet, in spite of this variety, there appears to be substantial regularities across predicates and subordinate clause structures that prior work has only scratched the surface of. Investigating the systematicities in how humans compute these inference patterns sheds light on how the human cognitive system constructs complex meanings from simpler parts and supports the development of intelligent computational systems for comprehending and reasoning about natural language in human-like ways.The current project approaches this investigation in two parts. First, it develops and deploys multiple scalable, crowd-sourced annotation protocols, based on experimental methodologies from psycholinguistics, in order to collect data about a wide variety of inference patterns triggered by all of the thousands of English predicates that combine with subordinate clauses. Second, it leverages recent advances in multi-task machine learning to build a unified computational model of the relationship between such predicates, the structure of their subordinate clauses, and the inferences that they trigger, which is trained on these data. This model not only helps to reveal systematicities in how humans compute the inference patterns of interest; it can also be straightforwardly incorporated into applied technologies for natural language understanding.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.

这个项目解决的是，当与从句结合的结构特征发生变化时，人类如何从数千个与从属从句结合的英语谓词中得出复杂的推论--“想”、“知道”、“说”、“告诉”、“记住”、“忘记”等。例如，“约翰忘了他买了牛奶”这句话与“约翰忘了买牛奶”这句话类似；但从第一句话中，听话者推断约翰买了牛奶，而从第二句话中，听话者推断他没有买牛奶。这种推理模式只是英语中许多此类句型中的一种；然而，尽管有各种各样的句型，谓词和从属结构之间似乎有很大的规律性，以前的工作只触及了其中的皮毛。研究人类如何计算这些推理模式的系统性有助于揭示人类认知系统如何从简单的部分构建复杂的含义，并支持以类似人类的方式理解和推理自然语言的智能计算系统的发展。本项目从两个部分来探讨这一研究。首先，它基于心理语言学的实验方法，开发和部署了多种可扩展的众包注释协议，以便收集由数千个与从属从句结合的所有英语谓词引发的各种推理模式的数据。其次，它利用多任务机器学习中的最新进展，建立了这样的谓词之间的关系、它们的从属子句的结构以及它们触发的推理之间的统一计算模型，该模型是在这些数据的基础上训练的。这个模型不仅有助于揭示人类如何计算感兴趣的推理模式的系统性，而且它也可以直接纳入自然语言理解的应用技术中。这个奖项反映了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。

项目成果

Natural Language Inference with Mixed Effects

• 发表时间: 2020-10
• 作者: William Gantt;Benjamin Kane;A. White

Lexicosyntactic Inference in Neural Models

• DOI: 10.18653/v1/d18-1501
• 发表时间: 2018-08
• 作者: Aaron Steven White;Rachel Rudinger;Kyle Rawlins;Benjamin Van Durme

Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation

• DOI: 10.18653/v1/d18-1007
• 发表时间: 2018-04
• 作者: Adam Poliak;Aparajita Haldar;Rachel Rudinger;J. E. Hu;Ellie Pavlick;Aaron Steven White;Benjamin Van Durme

On believing and hoping whether

关于相信和希望是否

• DOI: 10.3765/sp.14.6
• 发表时间: 2021
• 期刊: Semantics and Pragmatics
• 影响因子: 1.1
• 作者: White, Aaron Steven

Montague Grammar Induction

• DOI: 10.3765/salt.v30i0.4816
• 发表时间: 2020-07
• 期刊: ArXiv
• 作者: Gene Louis Kim;Aaron Steven White