Jordan Kodner

About

Jordan Kodner is an assistant professor in the Department of Linguistics at Stony Brook University, with affiliations to the Institute for Advanced Computational Science, the Department of Computer Science, the Institute for AI-Driven Discovery and Innovation, and the Natural Language Processing (NLP) group. His primary research focuses on computational approaches to child language acquisition and their broader implications. Specifically, he investigates algorithmic models of grammar acquisition, especially morphology, how these processes drive language variation and change, the insights they provide for low-resource NLP, and what they reveal about the intersection of NLP and cognitive science. Kodner is currently authoring a book titled "Child Language Acquisition in the Past: A Mechanistic View of Language Change" for Edinburgh University Press, which explores the role of child language acquisition in language change by integrating algorithmic modeling, corpus methods, historical linguistics, cognitive science, and variationist sociolinguistics. Kodner's academic background includes a PhD in Linguistics from the University of Pennsylvania, completed in 2020 under the supervision of Charles Yang and Mitch Marcus, and a master's degree from the University of Pennsylvania Department of Computer and Information Science obtained in 2018. Prior to his academic appointments, he worked as an Associate Scientist at Raytheon BBN Technologies from 2013 to 2015 on defense and…

Research topics

• Artificial Intelligence
• Computer Science
• Natural Language Processing
• Linguistics
• Psychology
• Mathematics
• Humanities
• Cognitive psychology
• Statistics
• Philosophy

Selected publications

• Miller's monkey updated: Communicative efficiency and the statistics of words in natural language

  Cognition · 2020 · 56 citations

  PublisherDOI

• SIGMORPHON–UniMorph 2022 Shared Task 0: Generalization and Typologically Diverse Morphological Inflection

  2022 · 26 citations

  1st authorCorresponding

  Jordan Kodner, Salam Khalifa, Khuyagbaatar Batsuren, Hossep Dolatian, Ryan Cotterell, Faruk Akkus, Antonios Anastasopoulos, Taras Andrushko, Aryaman Arora, Nona Atanalov, Gábor Bella, Elena Budianskaya, Yustinus Ghanggo Ate, Omer Goldman, David Guriel, Simon Guriel, Silvia Guriel-Agiashvili, Witold Kieraś, Andrew Krizhanovsky, Natalia Krizhanovsky, Igor Marchenko, Magdalena Markowska, Polina Mashkovtseva, Maria Nepomniashchaya, Daria Rodionova, Karina Scheifer, Alexandra Sorova, Anastasia Yemeli…

  DOI

• Why Linguistics Will Thrive in the 21st Century: A Reply to Piantadosi (2023)

  arXiv (Cornell University) · 2023-08-06 · 8 citations

  preprintOpen access1st authorCorresponding

  We present a critical assessment of Piantadosi's (2023) claim that "Modern language models refute Chomsky's approach to language," focusing on four main points. First, despite the impressive performance and utility of large language models (LLMs), humans achieve their capacity for language after exposure to several orders of magnitude less data. The fact that young children become competent, fluent speakers of their native languages with relatively little exposure to them is the central mystery…

  PublisherOA PDFDOI

• What learning Latin verbal morphology tells us about morphological theory

  Natural Language & Linguistic Theory · 2022-10-07 · 8 citations

  articleOpen access1st authorCorresponding

  Abstract The Classical Latin verb has featured prominently in theoretical morphology. In particular, the notoriously unpredictable forms of the past participles that nevertheless show reliable syncretism with a semantically diverse set of deverbals challenge our notions about the relationship between form and meaning. The various treatments of this system disagree not only in their theoretical building blocks but also in their basic assumptions about what ought to be explained, which makes it di…

  PublisherOA PDFDOI

• Morphological Inflection: A Reality Check

  2023-01-01 · 6 citations

  articleOpen access1st authorCorresponding

  Morphological inflection is a popular task in sub-word NLP with both practical and cognitive applications. For years now, state-of-the-art systems have reported high, but also highly variable, performance across data sets and languages. We investigate the causes of this high performance and high variability; we find several aspects of data set creation and evaluation which systematically inflate performance and obfuscate differences between languages. To improve generalizability and reliability…

  PublisherOA PDFDOI

Frequent coauthors

• Sarah R. Payne

  9 shared

• Salam Khalifa

  8 shared

• Spencer Caplan

  The University of Texas at Austin

  8 shared

• Charles Yang

  8 shared

• Chun Yang

  University of Science and Technology of China

  5 shared

• Christopher Cerezo Falco

  California University of Pennsylvania

  5 shared

• Natalia Krizhanovsky

  University of Massachusetts Amherst

  4 shared

• Nona Atanalov

  University of Massachusetts Amherst

  4 shared

Labs

• Jordan Kodner's LabPI

  Computational approaches to child language acquisition and their broader implications

Education

• Ph.D., Computer Science

  University of California, San Diego

  2008

• B.S., Computer Science

  University of California, San Diego

  2003

Similar researchers at Stony Brook University

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