CAREER: Abstract Universals in (Morpho)Syntax: Computational Characterizations and Empirical Implications

语言技术渗透到所有美国人的日常生活中：从垃圾邮件过滤器和拼写检查器到谷歌翻译和Alexa和Siri等数字助手。但并不是所有的语言社区都能同样受益。为了表现良好，目前的语言技术需要大量的数据，而这些数据对于较小的语言和方言，如纳瓦霍语或非裔美国人的白话英语来说是不可用的。这一要求与儿童的学习需求形成了鲜明对比，儿童不需要太多就能完美地掌握自己的母语。语言学家将语言习得的容易程度归因于天生的学习偏见--所有语言都有共同的属性，而意识到这些共同属性的学习算法需要的数据更少。但是，语言共性的语言模型是以术语表述的，这使得它们很难纳入当前的学习算法。在语言理论和语言技术之间架起一座桥梁，需要从数学和计算上理解共性，这两者都被纳入到这个项目中，以告知语言科学和语言技术。这个项目开发了一个句子结构领域的共性的计算模型，以及它是如何与单词的形状相互作用的。它采用了一个建立在理论计算机科学、数理逻辑和抽象代数技术基础上的框架。这使得能够以一种既严谨又足够灵活的方式来描述普遍性，从而很容易适应语言的丰富多样性。研究人员将把这一形式机制从声音推广到单词和句子。该项目借鉴了关于共性的语言学文献，并通过从计算分析中得出新的共性来为其提供支持。这一结果将允许新的机器学习算法将语言共性作为一种强烈的学习偏见。此外，该奖项还支持开发一个互动的在线学习平台。这一资源将使全国的语言学学生能够掌握通过计算镜头学习语言所需的技能。因此，该项目的研究和教育部分都服务于弥合语言科学和语言技术之间的差距。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。

Language technology permeates the daily life of all Americans: from spam filters and spellcheckers to Google translate and digital assistants like Alexa and Siri. But not all language communities benefit equally. In order to perform well, current language technology requires enormous amounts of data, which are not available for smaller languages and dialects such as Navajo or African American Vernacular English. This requirement stands in stark contrast to the learning needs of children, who do not require much to acquire their native language perfectly. Linguists have attributed the ease of language acquisition to innate learning biases--all languages share universal properties, and a learning algorithm that is aware of these universals requires less data. But linguistic models of language universals are couched in terms that make them hard to incorporate into current learning algorithms. Bridging the gap between linguistic theory and language technology requires a mathematically and computationally grounded understanding of universals, both of which are incorporated in this project to inform language science and language technology.This project develops a computational model of universals in the domain of sentence structure and how it interacts with the shape of words. It adopts a framework that is built on techniques from theoretical computer science, mathematical logic, and abstract algebra. This makes it possible to characterize universals in a manner that is both rigorous and sufficiently flexible to easily accommodate the rich diversity of languages. The researcher will generalize this formal machinery from sounds to words and sentences. The project draws from from the linguistic literature on universals, and feeds it by deriving new universals from the computational analysis. The results will allow for new machine learning algorithms that incorporate linguistic universals as a strong learning bias. In addition, this award supports the development of an interactive, online learning platform. This resource will enable linguistics students across the country to master the skills they need to study language through a computational lens. Both the research and the education component of this project thus serve the purpose of bridging the gap between language science and language technology.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.