Picture of John Pate I am a postdoctoral research fellow in the Department of Computing at Macquarie University. I study how people learn and use language, primarily from a computational perspective. I finished my PhD in 2013 at the School of Informatics at the University of Edinburgh. Sharon Goldwater supervised my PhD. Previously, I studied Linguistics at the Ohio State University.

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I am interested in how children induce linguistic structure from data. There are two parts to this question: what assumptions about grammatical form do children implicitly make (i.e., what are their learning biases), and what is the evidence they rely on? I use structured probabilistic models to quantify how well, and in what ways, different kinds of evidence disambiguate various grammatical forms. In previous and current work, I have looked at how acoustic cues, such as word duration, might provide useful evidence for identifying syntactic structure. I plan to start using such models to evaluate how well different assumptions about morphosyntactic abstraction match child language comprehension and production. I am also interested in expanding my research program to include laboratory experiments that follow up on computational results.

Dissertation Topic

For my dissertation ("Predictability effects in language acquisition"), which has been successfully defended and is under revision, I looked at how children might integrate different kinds of information to learn how to group words into syntactic phrases.

Specifically, I've been looking at two kinds of acoustic information. First, prosody (the rythm and intonation of speech) groups words together, such as words that would be written between commas or parentheses, and these groupings might serve as a sort of initial cue to syntactic groupings. Thus, acoustic cues to prosodic groupings might help with syntax acquisition. This is called the "Prosodic Bootstrapping" hypothesis and has been around as a proposal since at least the 1980's.

Second, it's been known for a long time that talkers tend to reduce words (i.e. pronounce more quickly and less distinctly) if they are highly predictable. More recently, it's been found that words are pronounced more quickly if they are in a low-probability syntactic structure. Thus, a reduced pronunciation provides a learner evidence that the hidden syntactic structure associated with that word is somehow unlikely. My dissertation introduces this as the "Predictability Bootstrapping" hypothesis.

My dissertation first provides some computational motivation for Predictability Bootstrapping as an especially easy kind of bootstrapping, shows that predictability effects exist in child-directed speech. Next, it provides computational modeling experiments that suggest both prosodic phrasing and predictability effects provide useful evidence for syntax acquisition.

Publications and Presentations

PhD Dissertation 2013 2011


I am the TA/Tutor/Marker for Computational Cognitive Science. I also filled this position in the 2010-2011 school year, when the course was introduced.


This code is released under the GPLv3, unless otherwise stated. It is released in the spirit of the CRAPL, to encourage replicability of results and reduce wasteful duplication of effort.

Some generally useful things

John K Pate
Building E6A Rm 324
Maquarie University
john.pate [at] mq.edu.au

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Just so you know, my student ID is key for one of the two easter eggs on this page.


Last updated: May 24 2013 05:36 GMT