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最大限度地提高人脑沟通便利性的设计选择。理解语言是如何优化(或不优化)进行交流的，对于开发人类语言理解和学习的科学模型至关重要。因此，对某些语言结构的目的的理解将有助于在第二语言教学中更有效地教授这些结构。此外，理解为什么语言是这样的，也将影响计算机系统对自然语言的理解。在过去的十年里，在计算机理解自然语言方面取得了重大进展，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这种简单的算法能够取得如此大的成就。一旦了解了这些属性，就有可能利用它们来开发更有效的算法。这项研究的重点是预测在大型标注文本语料库中观察到的语言的量化语序属性。数据来自通用依存关系项目，这是几所大学和谷歌最近合作开发的约40种语言的统一标注依存句法分析语料库。研究人员将开发简单的交流模型，其中包括交流中出错的概率和人类语言理解中的某些众所周知的难点(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。研究人员旨在使用这些模型来解释依存长度的分布(句法上相关单词之间的距离)和词序自由度的分布(单词可以在多大程度上以不同的顺序出现，同时保持相同的意义)。

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).