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Inductive learning of nonlocal phonological interactions

非局部语音交互的归纳学习

基本信息

批准号：
1724753
负责人：
Gillian Gallagher
金额：
$ 21.45万
依托单位：
New York University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-01 至 2023-01-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1724753&HistoricalAwards=false
关键词：
Inductive learning nonlocal phonological interactions

项目摘要

Language is a fundamental and universal aspect of human cognition. Linguistic research over the past five decades has established that language structure is governed by detailed rules. These rules constrain meanings, sentences, words, and sound patterns--the focus of the proposed research. For many years, sound structure was investigated primarily by theorizing. More recently, linguists have begun to test these theories using experiments and computational models. Computational models are valuable because they can only be created based on a complete and explicit understanding of the underlying rules. If the model succeeds in learning human-like rules when given the same data that is available to human learners, then it can shed some light on the rules that constitute the human knowledge of language and how humans learn these rules. In addition to helping scientists understand the human mind, computational models and the datasets they use are invaluable in developing applied computational tools for machine language translation, language identification, and artificial intelligence.The rules that govern sound patterns differ in nuanced ways between languages, and they can be divided into two kinds. First, all languages have rules that restrict how sounds interact with sounds that immediately precede or follow them: for example, in English, words can begin in "pr" but not "pn", whereas in Greek, words can begin in either sequence. But some languages also have rules that restrict the interactions of sounds that are not adjacent (nonlocal). Languages such as Hungarian and Turkish have vowel harmony, which means that all the vowels in a word tend to share certain features of their pronunciation. Navajo (Southwestern United States) has consonant harmony--consonants have to match in certain features. In languages such as Quechua (spoken in South America) and Amharic (Africa), certain features of consonants have to mismatch. Linguists have known about these patterns for a long time, and there are many theories of how they are cognitively represented. But these nonlocal rules continue to stymie computational models, because in order to notice them, the computer has to consider many more possibilities than it would for rules on adjacent sounds. This is similar to how much more difficult it is for a computer to crack a password the longer it gets. The proposed research builds a computational model of nonlocal rules that identifies certain clues to their existence in a language. The project will compile corpora to test the model's ability to find nonlocal rules (Quechua, Shona, Hungarian, Russian, Aymara, Sundanese). The model's performance will be compared with experiments with native speakers of several languages. The model, the corpora, and the experimental data will be made freely available to the scientific community and the public. Workshops will disseminate the research in Bolivia. The project will provide training for students in computational analysis and corpus building.

语言是人类认知的一个基本而普遍的方面。过去50年的语言学研究表明，语言结构是由详细的规则支配的。这些规则限制了意义、句子、单词和声音模式--这是拟议研究的重点。多年来，声音结构主要是通过理论化来研究的。最近，语言学家开始使用实验和计算模型来测试这些理论。计算模型是有价值的，因为它们只能基于对底层规则的完整和明确的理解来创建。如果该模型在获得人类学习者可用的相同数据时成功地学习了类似人类的规则，那么它可以揭示构成人类语言知识的规则以及人类如何学习这些规则。除了帮助科学家理解人类思维，计算模型和数据集在开发机器语言翻译、语言识别和人工智能的应用计算工具方面也是非常宝贵的。控制声音模式的规则在语言之间有细微的差异，它们可以分为两种。首先，所有的语言都有规则来限制声音与紧接在它们之前或之后的声音的相互作用：例如，在英语中，单词可以开始以“pr”开头，但不能以“pn”开头，而在希腊语中，单词可以以任何顺序开始开始。但有些语言也有规则限制不相邻（非本地）的声音之间的相互作用。匈牙利语和土耳其语等语言具有元音和谐，这意味着一个单词中的所有元音都倾向于共享其发音的某些特征。纳瓦霍语（美国西南部）有辅音和谐-辅音必须在某些特征上匹配。在克丘亚语（南美洲）和阿姆哈拉语（非洲）等语言中，辅音的某些特征必须不匹配。语言学家很早就知道这些模式，并且有许多关于它们如何被认知表示的理论。但是这些非局部规则继续阻碍计算模型，因为为了注意到它们，计算机必须考虑比相邻声音规则更多的可能性。这类似于计算机破解密码的难度越大，时间越长。拟议的研究建立了一个计算模型的非本地规则，确定某些线索，他们存在的语言。该项目将编译语料库，以测试该模型发现非本地规则（克丘亚语，绍纳语，匈牙利语，俄语，艾马拉语，巽他语）的能力。该模型的性能将与几种语言的母语使用者的实验进行比较。模型、语料库和实验数据将免费提供给科学界和公众。讲习班将在玻利维亚传播研究成果。该项目将为学生提供计算分析和语料库建设方面的培训。