Doctoral Dissertation Research: A Communicative Perspective on Quantitative Syntax

博士论文研究：数量句法的交际视角

• 批准号: 1551543
• 负责人: Edward Gibson
• 金额: $ 1.17万
• 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: 2016
• 资助国家: 美国
• 起止时间: 2016-02-15 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1551543&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Communicative; Perspective

This research addresses the question: why are human languages the way they are? The researchers propose that a number of the properties of languages can be explained as design choices that maximize ease of communication for the human brain. Understanding how languages are (or aren't) optimized for communication will be crucial to developing scientific models of human language understanding and learning. The resulting understanding of the purpose of certain language structures will enable more effective teaching of those structures in second language pedagogy. In addition, understanding why languages are the way they are will also affect computer systems for natural language understanding. Over the last decade there have been major advances in computer understanding of natural language, often using surprisingly simple algorithms. It is not known what properties of language make it possible to get so far with such simple algorithms. Once these properties are understood, it will be possible to leverage them to develop even more effective algorithms. The research focuses on predicting the quantitative word order properties of languages as observed in large annotated corpora of text. Data comes from the Universal Dependencies project, a recent collaboration of several universities and Google to develop uniformly annotated dependency-parsed corpora for about 40 languages. The researchers will develop simple models of communication that incorporate the probability of error in communication and certain well-known points of difficulty in human language comprehension (due to factors such as limited working memory resources). The frequency distribution of different word orders in the dependency corpora will be modeled by assuming that approximately rational speakers selects utterances which have a high probability of being understood. The researchers aim to use these models to explain the distribution of dependency length (distance between syntactically related words) and the distribution of the degree of word order freedom (the extent to which words can appear in different orders while keeping the same meaning).

这项研究解决了一个问题：为什么人类语言是这样的？研究人员提出，语言的许多属性可以解释为最大限度地方便人脑交流的设计选择。了解语言如何针对交流进行优化（或不优化）对于开发人类语言理解和学习的科学模型至关重要。 由此产生的对某些语言结构目的的理解将使第二语言教学中这些结构的教学更加有效。此外，理解语言为何如此也会影响计算机系统的自然语言理解。在过去的十年中，计算机对自然语言的理解取得了重大进展，通常使用令人惊讶的简单算法。目前尚不清楚语言的哪些特性使得如此简单的算法能够取得如此大的成果。一旦理解了这些属性，就可以利用它们来开发更有效的算法。该研究的重点是预测在大型注释文本语料库中观察到的语言的定量词序属性。数据来自通用依赖项项目，这是几所大学和 Google 最近合作的一个项目，旨在为大约 40 种语言开发统一注释的依赖项解析语料库。研究人员将开发简单的沟通模型，其中包含沟通错误的概率和人类语言理解中某些众所周知的困难点（由于工作记忆资源有限等因素）。依存语料库中不同词序的频率分布将通过假设近似理性的说话者选择具有高被理解概率的话语来建模。研究人员旨在利用这些模型来解释依存长度（句法相关单词之间的距离）的分布和词序自由度的分布（单词在保持相同含义的情况下可以以不同顺序出现的程度）。