Doctoral Dissertation Research: Finding Interrogativity
Doctoral Dissertation Research: Finding Interrogativity
批准号:
2140764
负责人:
Jeffrey Lidz
金额:
$1.08万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2022
资助国家:
美国
项目状态:
已结题
起止时间:
2022-03-01 至 2024-02-29
中文摘要
语言被用来进行各种各样的交际行为——提供信息、提出问题、提出要求等等。在任何语言中,句子的形式都有特定的信号,表明它通常用于什么交际行为。但这些信号因语言而异。这个项目研究儿童如何学会识别与他们说话的人的交流行为,同时学习与这些行为相关的表面信号。具体来说,这个项目调查了孩子们是如何产生联想疑问句的(“你想要一块饼干吗?”)到的问题。将疑问句映射到问题中呈现出有趣的习得挑战。首先,孩子们需要弄清楚他们的语言中疑问从句的形式:虽然所有的语言都倾向于有专门的疑问句,但这些疑问句的句法构成是不同的:在英语中,疑问句通常在词序上与陈述句不同(例如:“你想要一块饼干吗?”“我想要一块饼干”),但不是用普通话。为了弄清语言疑问句的形式,儿童需要注意形式的规律性。但是孩子们是如何知道哪些规则是重要的,他们又是如何将子句类型与它们的规范功能联系起来的呢?其次,孩子们需要弄清楚说话者什么时候在问问题(而不是断言或要求)。但是,如果他们还不能依靠这些行为通常使用的形式,他们怎么能做到这一点呢?先前的研究表明,尽管任务很复杂,但孩子们在三岁之前就能理解疑问句及其作为问题的常规功能。为了研究孩子们是如何解决这个复杂的映射问题的,这个项目研究了在对孩子说话时是如何提问的,使用什么形式,比较英语和普通话:父母提问的方式有多少正式的规律性?说话者的行为中是否存在可以泄露问题的语言外线索(例如,谈话中的停顿或注意力的集中)?该项目验证了这样一个假设,即儿童通过串联和相互信息的方式学习识别言语行为(问题)和从句类型(疑问句):儿童通过跟踪形式规则来学习识别疑问句,并结合他们对问题的不断增长的知识及其相关的社会线索;同样,他们通过追踪社会线索来识别问题,并结合他们对疑问语法的理解。该项目将包括两个语料库研究和一个计算模型:研究1研究英语父母和孩子之间的对话,研究2研究普通话父母和孩子之间的对话,其中父母的话语将被注释为语用,韵律和句法特征。然后将使用注释数据通过计算建模来测试这种语用语法自举假设的可行性(研究3)。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
Language is used to perform various kinds of communicative acts - providing information, asking questions, making requests, etc. In any language, there are specific signals in the form of a sentence that indicates what communicative act it is typically used for. But these signals vary from language to language. This project examines how children learn to identify the communicative acts of the people speaking to them while learning the surface signals associated with these acts. Specifically, this project investigates how children come to associate interrogatives ("Do you want a cookie?") to questions. Mapping interrogatives to questions present interesting acquisition challenges. First, children need to figure out the form of interrogative clauses in their language: while all languages tend to have dedicated clauses for questions, the syntactic makeup of these interrogatives differ: in English, interrogatives usually differ from declaratives in word order (e.g. "Do you want a cookie" vs. "I want a cookie"), but not in Mandarin. To figure out the form of their language’s interrogatives, children need to notice regularities in form. But how do children figure out which regularities matter, and how do they associate clause types with their canonical function? Second, children need to figure out when speakers are asking questions (vs. making assertions or requests). But how can they do so, if they can’t yet rely on the forms typically used for these acts? Previous research shows that, despite the complexity of the task, children understand interrogatives and their conventionalized function as questions before age three. To investigate how children solve this complex mapping problem, this project examines how questions are asked in speech to children, using what forms, comparing English and Mandarin: how much formal regularity is there in how parents ask questions? And are there extralinguistic cues in speakers’ behavior that can give away questionhood (e.g., pauses in conversation or focus of attention)? The project tests the hypothesis that children learn to identify speech act (question) and clause type (interrogative) in tandem and mutually informative ways: children learn to identify interrogatives by tracking formal regularities in conjunction with their growing knowledge of questionhood and its associated social cues; similarly, they identify questions by tracking social cues in conjunction with their growing understanding of interrogative syntax. The project will consist of two corpus studies and one computational model: Study 1 examines conversations between English-speaking parent-child dyads and Study 2 conversations between Mandarin parent-child dyads, where parent utterances will be annotated for pragmatic, prosodic, and syntactic features. The annotated data will then be used to test the viability of this pragmatic syntactic bootstrapping hypothesis via computational modeling (Study 3).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.
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