Doctoral Dissertation Research: A Communicative Perspective on Quantitative Syntax

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，语言的许多特性可以解释为设计选择，最大限度地方便人类大脑的沟通。了解语言如何优化（或不优化）交流对于开发人类语言理解和学习的科学模型至关重要。 对某些语言结构的目的的理解将有助于在第二语言教学法中更有效地教授这些结构。此外，理解为什么语言是它们的方式也将影响计算机系统的自然语言理解。在过去的十年里，计算机理解自然语言方面取得了重大进展，通常使用的算法非常简单。目前还不知道语言的什么属性使得可以用这样简单的算法得到这么多。一旦理解了这些特性，就有可能利用它们来开发更有效的算法。该研究的重点是预测语言的定量词序属性，如在大型注释文本语料库中观察到的。数据来自Universal Dependencies项目，这是几所大学和Google最近合作开发的约40种语言的统一注释依赖关系解析语料库。研究人员将开发简单的沟通模型，其中包括沟通中的错误概率和人类语言理解中的某些众所周知的困难点（由于工作记忆资源有限等因素）。依赖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人选择具有高概率被理解的话语来建模。研究人员的目标是使用这些模型来解释依赖长度的分布（句法相关的单词之间的距离）和词序自由度的分布（单词在保持相同含义的同时可以以不同顺序出现的程度）。

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).