Verb Learning and The Early Development of Sentence Comprehension: Experimental and Computational Studies

动词学习和句子理解的早期发展：实验和计算研究

• 批准号: 1348522
• 负责人: Cynthia Fisher
• 金额: $ 19.02万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1348522&HistoricalAwards=false
• 关键词: Verb; Learning; Early; Development; Sentence

Understanding how children learn language--how they gather data from language experience and use it to uncover linguistic structure--is a key challenge for cognitive science, in part because native mastery of a language is one of the foundations of both formal and informal education. Infant language learners encounter sentences paired with world situations; at the start, these sentences are made up of unknown words, combined by the rules of an unknown grammar. Based on such data, toddlers begin to understand sentences early in the second year of life, and ultimately build a lexicon and grammar that support nearly unlimited generalization to new sentences. Accounts of how children begin to understand sentences necessarily begin with the non-linguistic world. The infant, not yet knowing the words or the grammar, must figure out what words and sentences mean in large part by observing the world situations in which they occur. But aspects of the meanings of verbs challenge the assumption that learners can straightforwardly recover verb (and thus sentence) meanings from situations. This problem inspired the syntactic bootstrapping theory, which proposes that children use their growing knowledge of syntax itself to learn verbs and interpret sentences. This research is will provide important insight into normal language acquisition, but it may, in the future, contribute also to the diagnosis and treatment of developmental language disorders. In this project, Dr. Fisher and Dr. Roth explore how syntactic bootstrapping works, and how it begins, extending the structure-mapping account of the origins of syntactic bootstrapping. On this account, infants approach language armed with an innate bias toward one-to-one mapping between nouns in sentences and participant-roles in events. Given this bias, children find the number of nouns in a sentence inherently meaningful: For example, as soon as children can identify some nouns, they can assign different interpretations to transitive and intransitive verbs, essentially by counting the nouns. A corollary of this account is that children identify words as verbs by learning their syntactic combinatorial properties. This project asks how syntactic bootstrapping scales up to the complexity of verbs' predicate-argument structures and the ambiguity of sentences. The project addresses two linked proposals, by combining verb-learning experiments with children and experiments with a computational model based on systems for Semantic Role Labeling (SRL). The first proposal is that distributional learning creates detailed syntactic-semantic combinatorial knowledge about verbs. This knowledge plays two roles: (a) it permits syntactic bootstrapping, as children use verbs' combinatorial behavior to identify them as verbs, and to compute their semantic structure; and (b) it supports online sentence processing, by reducing ambiguity and improving children's sentence representations (this is known as 'verb bias'). The second proposal is that an expectation of discourse continuity facilitates verb learning by letting learners gather evidence for verb argument-structure across nearby sentences. Combinatorial learning about verbs guides this process, by cuing children to seek referents for missing arguments in the discourse context.

了解儿童如何学习语言--他们如何从语言经验中收集数据，并利用这些数据来揭示语言结构--是认知科学面临的一个关键挑战，部分原因是母语掌握一门语言是正规和非正规教育的基础之一。幼儿语言学习者会遇到与世界情景成对的句子；一开始，这些句子是由未知单词组成的，由未知语法的规则组合而成。基于这样的数据，幼儿在第二年就开始理解句子，并最终建立起支持对新句子几乎无限概括的词汇和语法。关于儿童如何开始理解句子的描述必须从非语言世界开始。还不知道单词或语法的婴儿，必须通过观察单词和句子出现的世界情景来弄清楚它们在很大程度上意味着什么。但动词意义的某些方面挑战了这样一种假设，即学习者可以直接从情景中恢复动词(以及句子)的意义。这个问题启发了句法自举理论，该理论认为儿童使用他们不断增长的句法知识本身来学习动词和解释句子。这项研究将为正常的语言习得提供重要的洞察力，但它也可能在未来对发育性语言障碍的诊断和治疗做出贡献。在这个项目中，Fisher博士和Roth博士探索了句法自举是如何工作的，以及它是如何开始的，扩展了句法自举起源的结构映射解释。在这一点上，婴儿在接触语言时天生倾向于句子中的名词与事件中的参与者角色之间的一对一映射。鉴于这种偏见，孩子们发现一个句子中名词的数量具有内在的意义：例如，一旦孩子们能识别出一些名词，他们就能给及物动词和不及物动词赋予不同的解释，基本上是通过计算名词。这一解释的一个推论是，儿童通过学习单词的句法组合属性来将单词识别为动词。这个项目询问句法自举如何适应动词谓词论元结构的复杂性和句子的多义性。该项目提出了两个相互关联的建议，将儿童动词学习实验与基于语义角色标记系统(SRL)的计算模型实验相结合。第一种建议是，分布式学习创造了关于动词的详细的句法-语义组合知识。这种知识起到两个作用：(A)它允许句法自举，因为儿童使用动词的组合行为来识别它们是动词，并计算它们的语义结构；(B)它通过减少歧义和改善儿童的句子表征(这被称为“动词偏向”)支持在线句子处理。第二个建议是，对语篇连续性的期望通过让学习者在邻近的句子中收集动词论元结构的证据来促进动词学习。关于动词的组合学习通过提示儿童在语篇语境中寻找缺失论点的参照物来指导这一过程。