Doctoral Dissertation Research: A Communicative Perspective on Quantitative Syntax

Massachusetts Institute Of Technology, Cambridge MA

Investigators

Abstract

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).