Jon Rawski

Jon Rawski

Assistant Professor

San Jose State University

About

I am an Assistant Professor of Linguistics at San Jose State University, where I teach courses on general and computational linguistics. My work generally concerns the mathematics of human language and learning. These cognitive feats emerge from humans' unique neuronal structure and computing power, allowing linguistic insight to contribute to the broader cognitive sciences and artificial intelligence.

Interests

  • Computational/Mathematical Linguistics
  • Cognitive Science
  • Artificial Intelligence

Education

  • PhD in Linguistics, 2021

    Stony Brook University

  • MSci in Cognitive Science, 2016

    Higher School of Economics

  • BA in Linguistics, 2013

    University of Minnesota

Recent & Upcoming Presentations

See my CV for a full list of all my talks.

Abductive Learning of Phonotactic Constraints

I describe general properties of phonotactic learning via a series of non-statistical abductive inference algorithms
Sep 17, 2021 12:00 PM Invited talk, UC Berkeley Phorum
Slides
Abductive Learning of Phonotactic Constraints

Typology Emerges from Computability

I describe how the scope and limits of linguistic typology are structured by restricted types of computation.
Jun 4, 2021 12:00 PM Invited talk, ACL SIGTYP Lecture Series
Slides
Typology Emerges from Computability

Overcoming Poverty of Stimulus with Structure and Parameters

We show how phonological typology computationally emerges from combinations of relativized representations and the simplest of a suite of online learning algorithms.
May 8, 2020 2:00 PM Workshop on Theoretical Phonology (WTPh)
Slides Video
Overcoming Poverty of Stimulus with Structure and Parameters

Projects & Publications

*
All Sign Language Computational Phonology Learning Algorithms Other

Structure and Learning in Natural Language

My doctoral dissertation, examining the relationship between abductive inference and algebraically structured hypothesis spaces, giving a general form for grammar learning over arbitrary linguistic structure.
PDF PDF
Structure and Learning in Natural Language

Typology Emerges from Simplicity in Representations and Learning

We derive the well-studied subregular classes of formal languages, which computationally characterize natural language typology, purely from the perspective of algorithmic learning problems.
PDF Open-Access Pub
Typology Emerges from Simplicity in Representations and Learning

Talk isn't so Cheap

Invited response commenting on substantive and computational comparisons of spoken and signed languages
Official Pub Figure 1 Figure 2 Figure 3 Figure 4 Figure 5
Talk isn't so Cheap

Comment on Nonadjacent Dependency Processing in Monkeys, Apes, and Humans

We comment on non-human animals' ability to learn syntactic vs phonological dependencies in pattern-learning experiments.
Official Pub
Comment on Nonadjacent Dependency Processing in Monkeys, Apes, and Humans

Computational Restrictions on Iterative Prosodic Processes

We formalize various iterative prosodic processes including stress, syllabification and epenthesis using logical graph transductions, showing that the necessary use of fixed point operators without quantification restricts them to a structured subclass of subsequential functions.
PDF Poster Official Pub
Computational Restrictions on Iterative Prosodic Processes

Computational Locality in Nonlinear Morphophonology

We present an automata-theoretic analysis of locality in nonlinear phonology and morphology.
Review Draft
Computational Locality in Nonlinear Morphophonology

Strong Generative Capacity of Morphological Processes

We analyze divergences in strong generative capacity between morphological processes that are equivalent in weak generative capacity.
PDF
Strong Generative Capacity of Morphological Processes

The Computational Power of Harmony

We overview vowel harmony computationally, describing necessary and sufficient conditions on phonotactics, processes, and learning.
PDF Review Draft
The Computational Power of Harmony

Phonological Abstractness in the Mental Lexicon

We overview the notion of phonological abstractness, various types of evidence for it, and consequences for linguistics and psychology.
Review Draft
Phonological Abstractness in the Mental Lexicon

The Logical Nature of Phonology Across Speech and Sign

This article examines whether the computational properties of phonology hold across spoken and signed languages, using model theory and logical transductions.
Review Draft Invited Talk Slides Learning Slides
The Logical Nature of Phonology Across Speech and Sign

What can formal language theory do for animal cognition studies?

We comment on mathematical fallacies present in artificial grammar learning experiments and suggest how to integrate psycholinguistic and mathematical results.
Official Pub
What can formal language theory do for animal cognition studies?

Probing RNN Encoder-Decoder Generalization of Subregular Functions using Reduplication

We analyze the expressivity of a variety of recurrent encoder-decoder networks, showing they are limited to learning subsequential functions, and connecting RNNs with attention mechanisms to a class of deterministic 2-way transducers.
PDF Code Slides Official Pub
Probing RNN Encoder-Decoder Generalization of Subregular Functions using Reduplication

Multi-Input Strictly Local Functions for Templatic Morphology

We provide an automata-theoretic characterization of templatic morphology, extending strict locality to consider n-ary functions.
PDF Slides Official Pub
Multi-Input Strictly Local Functions for Templatic Morphology

Multi-Input Strictly Local Functions for Tonal Phonology

We provide an automata-theoretic characterization of tonal phonology, extending strict locality to consider n-ary functions.
PDF Poster Official Pub
Multi-Input Strictly Local Functions for Tonal Phonology

History of Phonology: Learnability

This chapter examines the brief but vibrant history of learnability in phonology.
PDF
History of Phonology: Learnability

Tensor Product Representations of Subregular Formal Languages

I provide a vector space characterization of the Star-Free and Locally Threshold testable classes of formal languages, over arbitrary data structures.
PDF IJCAI slides NECPhon slides
Tensor Product Representations of Subregular Formal Languages

Learning with Partially Ordered Representations

We describe a partial order on the space of model-theoretic constraints and a learning algorithm for constraint inference.
PDF Poster Slides DOI Official Pub
Learning with Partially Ordered Representations

Finite-State Locality in Semitic Root-and-Pattern Morphology

We describe the finite-state nature of root-and-pattern morphology using Semitic as a case study, and discuss issues of finiteness vs. infinity, and template emergence.
PDF DOI
Finite-State Locality in Semitic Root-and-Pattern Morphology

No Free Lunch in Linguistics or Machine Learning: Reply to Pater

We caution about confusing ignorance of biases with absence of biases in machine learning and linguistics, especially for neural networks.
PDF Official Pub
No Free Lunch in Linguistics or Machine Learning: Reply to Pater

Quantified Sentences as a Window into Prediction and Priming: An ERP Study

We used event related potentials (ERPs) to examine the processing of quantified sentences in an auditory/visual truth value judgment task, specifically to probe truth value and quantifier type influences on the N400 and ERP markers of quantifier complexity.
PDF CLS Slides SNL Poster
Quantified Sentences as a Window into Prediction and Priming: An ERP Study

Phonological Complexity is Subregular: Evidence from Sign Language

I show the complexity of several signed processes is subregular across speech and sign using string representations.
PDF Slides
Phonological Complexity is Subregular: Evidence from Sign Language

Employment

 
 
 
 
 

Assistant Professor

San Jose State Linguistics Dept.

Jul 2021 – Present
 
 
 
 
 

Graduate Assistant

Stony Brook Center for Neural Circuit Dynamics

Jan 2019 – Jan 2021
 
 
 
 
 

Research Assistant

Stony Brook Computational Linguistics Lab

Jun 2018 – Jun 2021
 
 
 
 
 

Research Fellow

Institute for Advanced Computational Science

Sep 2017 – Jun 2021
 
 
 
 
 

Graduate Assistant

HSE Theoretical Neuroscience Group

Sep 2015 – Jul 2016
 
 
 
 
 

Research Assistant

HSE Neurolinguistics Laboratory

Jan 2015 – May 2015
 
 
 
 
 

Undergraduate Researcher

UMN Dept. of Speech-Language-Hearing Sciences

Jan 2013 – Aug 2013
 
 
 
 
 

Student Manager, Research Assistant

NuMI Off-Axis Electron Neutrino Appearance (NOvA) Laboratory, UMN Physics Dept.

Jun 2012 – Aug 2013