Doctoral Dissertation Research: Extending and testing theories of language production by investigating speaker choice in a classifier language

博士论文研究：通过研究分类语言中说话人的选择来扩展和测试语言产生的理论

• 批准号: 1844723
• 负责人: Roger Levy
• 金额: $ 1.84万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-01 至 2021-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1844723&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Extending; testing

Natural language often gives speakers multiple ways to convey the same meaning. Meanwhile, linguistic communication takes place in the face of environmental and cognitive constraints. When multiple options are available to express more or less the same meaning, what general principles govern speaker choice? Advancing our understanding of this question can potentially enhance a broad array of human language technologies, such as providing more human-like language generation with better understanding of speaker choice, more accurate machine translation, better resources for language learning and teaching, as well as insights to improve treatment for language disorders. Within this broader research program, this project focuses on the influence of contextual predictability on the encoding of linguistic content manifested by speaker choice in a classifier language. In English, a numeral modifies a noun directly (e.g., three tables). In classifier languages such as Mandarin Chinese, it is obligatory to use a classifier (CL) with the numeral and the noun (e.g., three CL.flat table, three CL.general table). While different nouns are compatible with different specific classifiers, there is a general classifier 'ge' (CL.general) that can be used with most nouns. This study focuses on the alternating options between using the general classifier versus a specific classifier with the same noun where the options are nearly semantically invariant. The use of a more specific classifier would reduce surprisal at the noun, but the use of that more specific classifier may be dispreferred from a production standpoint if accessing the general classifier requires less effort. This project combines corpus analyses, psycholinguistic behavioral experiments and computational modeling using techniques from statistics, natural language processing, and experimental psychology, examining how language users allocate resources to prepare them to produce and comprehend language, shedding lights on why language is structured in the way it is.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.

自然语言通常为说话者提供多种方式来传达相同的含义。同时，语言交流是在环境和认知约束下进行的。当有多个选项可以表达或多或少相同的含义时，什么一般原则可以指导说话人的选择？加深我们对这个问题的理解可能会增强一系列广泛的人类语言技术，例如提供更接近人类的语言生成，更好地理解说话者的选择、更准确的机器翻译、更好的语言学习和教学资源以及见解改善语言障碍的治疗。在这个更广泛的研究计划中，该项目重点关注语境可预测性对分类器语言中说话者选择所体现的语言内容编码的影响。在英语中，数字直接修饰名词（例如，三个表）。在普通话等分类词语言中，必须使用带有数字和名词的分类词（CL）（例如，三个 CL. 平表，三个 CL. 普通表）。虽然不同的名词与不同的特定分类器兼容，但有一个通用分类器“ge”（CL.general）可以与大多数名词一起使用。本研究重点关注使用通用分类器与具有相同名词的特定分类器之间的交替选项，其中选项几乎在语义上不变。使用更具体的分类器会减少对名词的惊讶，但是如果访问通用分类器需要较少的努力，那么从生产的角度来看，使用更具体的分类器可能是不受欢迎的。该项目结合了语料库分析、心理语言学行为实验和计算建模，利用统计学、自然语言处理和实验心理学等技术，研究语言用户如何分配资源以准备他们产生和理解语言，揭示语言为何以这种方式构建确实如此。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值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

Availability-Based Production Predicts Speakers’ Real-time Choices of Mandarin Classifiers

基于可用性的生产可预测演讲者的普通话分类器的实时选择

• 发表时间: 2019
• 期刊: Proceedings of the Annual Conference of the Cognitive Science Society
• 作者: Zhan, Meilin;Levy, Roger
• 通讯作者: Levy, Roger