Jordan Kodner on the Web

email: jordan.kodner at-sign stonybrook edu
office: Dept. of Linguistics, Social & Behavioral Sciences Building (SBS), Floor 2

Spring 2026 Information
About Me
Journal Articles
Proceedings
Other Manuscripts
Presentations

I am an assistant professor in the Stony Brook University Department of Linguistics, affiliated with the Institute for Advanced Computational Science, Department of Computer Science, Institute for AI-Driven Discovery and Innovation, and Natural Language Processing (NLP) group. My primary research revolves around computational approaches to child language acquisition and their broader implications. In particular,

Algorithmic models of grammar acquisition, especially morphology
How those processes drive language variation and change
What insights they provide for low-resource NLP
What they can tell us about the the intersection of NLP and cognitive science

I am writing a book titled Child Language Acquisition in the Past: A Mechanistic View of Language Change for Edinburgh University Press. It investigates the role that child language acquisition plays in language change. The broad goal is to build a general understanding of language change grounded in an understanding of its mechanisms. Enabled by algorithmic modeling and corpus methods, I draw insights from traditional historical linguistics, the cognitive science of language acquistion, and findings in variationist sociolinguistics.

My other academic interests, a few of which have intersected my research so far, include (alphabetically): Chinese language varieties, computing history, evolutionary theory, formal language theory, general NLP, human geography, Indo-European historical linguistics, Latin language, paleontology and cladistics, programming languages and software engineering, Roman history and culture, Semitic languages, Shona language, Singlish and Singaporean English, and writing systems.

I received my PhD in 2020 (my dissertation) from the University of Pennsylvania Department of Linguistics, where I worked with Charles Yang and Mitch Marcus. I received a master's degree from the University of Pennsylania Department of Computer and Information Science in 2018. From 2013 through 2015, I was an Associate Scientist in the Speech, Language, and Multimedia group at Raytheon BBN Technologies where I worked on defense and medical-related projects. I interned with Amazon Alexa AI-Natural Language Understanding in summer 2020.

Sarah Payne, and Jordan Kodner. (2025). Some Innate Characteristics of Neural Network Models of Morphological Inflection. Linguistics Vanguard. (manuscript)
Jordan Kodner, Sarah Payne, Jeff Heinz. (2023). Why Linguistics Will Thrive in the 21st Century: A Reply to Piantadosi (2023).

Current Courses

LIN 335: Language Acquisition Monday/Wednesday 11:00-12:20pm.

The official description:
An introduction to computational linguistics for students with previous programming experience. This course explores the models, algorithms, and techniques that dominate modern-day language technology, and it evaluates them from a linguistically informed perspective. Topics include corpus-based methods, finite-state approaches, machine learning, and model evaluation techniques. Great emphasis is put on discussing the limitations of existing techniques and how they might benefit from linguistic insights. Students will also hone their programming skills and develop familiarity with state-of-the-art software packages for computational linguistics. Formerly offered as LIN 220; not for credit in addition to LIN 220.

SBCs: STEM+

LIN 655: Computational Linguistics Seminar: Variationist Sociolinguistics Monday/Wednesday 3:30-4:50pm.

A description:
The spring 2026 iteration of LIN 655 Computational Linguistics Seminar will be an introduction to sociolinguistics from a quantitative variationist perspective, with a special focus on computational modeling methodologies. Variation, which is observable across all levels of linguistic structure and derives from a range of distinct mechanisms, is an inescapable part of language use. For theoretical and computational linguists, variationist sociolinguistics provides a framework for reasoning about and accounting for the types of variation that we inevitably encounter when working with naturalistic language corpora and other forms of real-world data.

Traditional quantitative variationist analysis makes use of tools from linguistic fieldwork, corpus linguistics, and acoustic phonetics to uncover linguistic variables and their statistical relationships with speaker demographics and speech contexts. In addition to covering these approaches, this seminar will move beyond them to survey two broader computational modeling areas: computational modeling of social networks and speech communities and computational learning models in the context of variation in the input. These lesser known research areas extend the computational base of sociolinguistics.

General Office Hours: Tuesday 10:00am-1:00pm

Sarah Payne, and Jordan Kodner. (2025). Some Innate Characteristics of Neural Network Models of Morphological Inflection. Linguistics Vanguard. (manuscript)
Jordan Kodner, Sarah Payne, Salam Khalifa, and Zoey Liu. (2025). Evaluating Learning Trajectories of Neural Morphology Acquisition Models. Linguistics Vanguard. (manuscript)
Jordan Kodner. (2025). “Generative Perspectives of Language Change and Acquisition. In Diachrony and Language Evolution in International Encyclopedia of Language and Linguistics, 3rd Edition. (manuscript)
Héctor J. Vázquez Martínez, Annika Heuser, Charles Yang, Jordan Kodner. (to appear). Evaluating the Existence Proof: LLMs as Cognitive Models of Language Acquisition. In In Artificial Knowledge of Language.
Jordan Kodner. (2023). Laissez-Faire Analogical Change. Journal of Historical Syntax. 7(6-19). Proceedings of the 22nd Diachronic Generative Syntax (DiGS) Conference.
Jordan Kodner. (2022). Language Acquisition Guiding Theory and Diachrony: A Case Study from Latin Morphology. Natural Language and Linguistic Theory, 41:733–792.
Jordan Kodner, Spencer Caplan, and Charles Yang. (2022). Another model not for the learning of language. Proceedings of the National Academy of Science, 119(29):e2204664119.
Jordan Kodner. (2022). Computational Approaches to Morphology Acquisition. In Oxford Research Encyclopedia in Linguistics. Oxford University Press.
{Spencer Caplan, Jordan Kodner}, and Charles Yang. (2020). Miller's Monkey Updated: Communicative Efficiency and the Statistics of Words in Natural Language. Cognition, 205:104466. (manuscript | source code | demo)
Jordan Kodner. (2020). Modeling Language Change in the St. Louis Corridor. Language Variation and Change. 32(1). (manuscript | supplementary material [incorrect on LVC website] | source code)
Kathryn Schuler, Jordan Kodner, and Spencer Caplan. (2020). Abstractions are good for brains and machines: A commentary on Ambridge (2020). First Language, 40(5-6):631–635.
Jordan Kodner. (2019). Estimating child linguistic experience from historical corpora. Glossa: A Journal of General Linguistics, 4(1):122.

Note: Proceedings published in the ACL conference and workshop anthologies (ACL, ACL Findings, CMCL, ComputEL, EMNLP, GEM2, GenBench, Insights, LChange, LREC, SCiL, SIGMORPHON, VarDial, WANLP) are refereed and archival. CogSci proceedings are refereed but non-archival.

2025

Sarah Payne and Jordan Kodner. (2025). Lemmas Matter, But Not Like That: Predictors of Lemma-Based Generalization in Morphological Inflection. In Findings of the Association for Computational Linguistics: ACL 2025, pages 25270-25286.
Zhengxiang Jack Wang, Jordan Kodner, and Owen Rambow. (2025). Evaluating LLMs with Multiple Problems at Once. In Proceedings of the Fourth Workshop on Generation, Evaluation and Metrics (GEM2), pages 178-199.
Zhengxiang Jack Wang, Veronica Makarova, Zhi Li, Jordan Kodner, and Owen Rambow. (2025). LLMs can Perform Multi-Dimensional Analytic Writing Assessments: A Case Study of L2 Graduate-Level Academic English Writing. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8637-8663.
Zhengxiang Jack Wang, Jordan Kodner, and Owen Rambow. (2025). Exploring Limitations of LLMs' Text Understanding Capabilities with Multi-Problem Evaluation. In The 2025 Workshop of Insights from Negative Results in NLP (Insights), pages 121-140.
Salam Khalifa, Abed Qaddoumi, Jordan Kodner, and Owen Rambow. (2025). Learning Cross-Dialectal Morphophonology with Syllable Structure Constraints. In The Twelfth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial), pages 157–16.

2023

Jordan Kodner, Salam Khalifa, Sarah Payne. (2023). Exploring Linguistic Probes for Morphological Generalization. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8933–8941.
Héctor J. Vázquez Martínez, Annika Heuser, Charles Yang, Jordan Kodner. (2023). Evaluating Neural Language Models as Cognitive Models of Language Acquisition. In The first workshop on (benchmarking) generalisation in NLP (GenBench), pages 48-64.
Jordan Kodner, Sarah Payne, Salam Khalifa, Zoey Liu. (2023). Morphological Inflection: A Reality Check. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), pages 6082-6101.
Salam Khalifa, Sarah Payne, Jordan Kodner, Ellen Broselow, Owen Rambow. (2023). A Cautious Generalization Goes a Long Way: Learning Morphophonological Rules. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), pages 1793–1805.
Jordan Kodner, Salam Khalifa, Sarah Payne, Zoey Liu. (2023). Re-Evaluating Neural Models of Morphological Inflection. In Proceedings of the 45th Annual Meeting of the Cognitive Science Society (CogSci), 45:3259– 3267.

2022

Salam Khalifa, Jordan Kodner, Owen Rambow. (2022). Learning Arabic Morphophonology. In Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP), pages 295-301.
Jordan Kodner, Salam Khalifa, et xxviii al. (2022). SIGMORPHON-UniMorph 2022 Shared Task 0: Generalization and Typologically Diverse Morphological Inflection. In Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 176-203.
Jordan Kodner and Salam Khalifa. (2022). SIGMORPHON-UniMorph 2022 Shared Task 0: Modeling Inflection in Language Acquisition. In Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 157-175.
Jordan Kodner. (2022). Modeling the Relationship between Input Distributions and Learning Trajectories with the Tolerance Principle. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics (CMCL), pages 61-67.
Aniket Kali & Jordan Kodner. (2022). Language Acquisition, Neutral Change, and Diachronic Trends in Noun classifiers. In Proceedings of the 3rd Workshop on Computational Approaches to Historical Language Change (LChange), pages 11-22.

2021

Caleb Belth, Sarah Payne, Deniz Beser, Jordan Kodner, & Charles Yang. (2021). The Greedy and Recursive Search for Morphological Productivity. In Proceedings of the 43th Annual Meeting of the Cognitive Science Society (CogSci). 43:2869-2875.
Sarah Payne, Jordan Kodner, & Charles Yang. (2021). Learning Morphological Productivity as Meaning-Form Mappings. In Proceedings of the Annual Meeting of the Society for Computation in Linguistics 2021 (SCiL). pages 177-187.

2020

Jordan Kodner & Nitish Gupta. (2020). Overestimation of Syntactic Representation in Neural Language Models. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), pages 1757-1762.
Hongzhi Xu, Jordan Kodner, Mitchell Marcus, & Charles Yang. (2020). Modeling Morphological Typology for Unsupervised Learning of Language Morphology. In Proceedings of the Annual Meeting of the 58th Association for Computational Linguistics (ACL), pages 6672-6681.
Justin Mott, Ann Bies, Stephanie Strassel, Jordan Kodner, Caitlin Richter, Hongzhi Xu, & Mitchell Marcus. (2020). Morphological Segmentation for Low Resource Languages. In Proceedings of the Twelfth Language Resources and Evaluation Conference (LREC), pages 3996-4002.
Jordan Kodner & Caitlin Richter. (2020). Transparent /aɪ/-Raising as a Contact Phenomenon. In Penn Working Papers in Linguistics: Papers from NWAV, 25(2):61-70.

2019

Sun Jae Lee & Jordan Kodner. (2019). Acquiring the Korean Causatives. In Proceedings of the Chicago Linguistics Society Conference (CLS), 55(1):259-268.

2018

Shyam Upadhyay, Jordan Kodner & Dan Roth. (2018). Bootstrapping Transliteration with Guided Discovery for Low-Resource Languages. in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 501-511.
Spencer Caplan & Jordan Kodner. (2018). The Acquisition of Vowel Harmony from Simple Local Statistics. In Proceedings of the 40th Annual Meeting of the Cognitive Science Society (CogSci), 40:1442-1447.
Jordan Kodner & Christopher M Cerezo Falco. (2018). A Framework for Representing Language Acquisition in a Population Setting. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL), pages 1149-1159.
Jordan Kodner. (2018). Modeling Representational Constraints in Word Segmentation. In Proceedings of the 42nd annual Boston University Conference on Language Development (BUCLD), 42(1):391-399.
Jordan Kodner. (2018). Syntactic Category Learning as Iterative Prototype-Driven Clustering. InProceedings for the Society for Computation in Linguistics (SCiL) 2018, pages 44-54.

2017

Jordan Kodner, Spencer Caplan, Hongzhi Xu, Mitch Marcus & Charles Yang. (2017). Case Studies in the Automatic Characterization of Grammars from Small Wordlists. In Proceedings for the 2nd Workshop on Computational Methods for Endangered Languages (ComputEL), pages 76-84.

Jordan Kodner, Sarah Payne, Jeff Heinz. (2023). Why Linguistics Will Thrive in the 21st Century: A Reply to Piantadosi (2023).
Jordan Kodner. (2020). Language Acquisition in the Past. PhD thesis, University of Pennsylvania.
Jordan Kodner. (2016). Shona Subjects are Subjects.

2026

(invited). Jordan Kodner. (Title TBD). CUNY Cognitive Science Colloquium, CUNY Graduate Center, New York.

Matt Malone, Jordan Kodner, & Spencer Caplan. "Effects and Causes: On the Semantic Coherence of Swahili Noun Classes." LSA 2026, New Orleans, and PLC 50, Philadelphia.
2025
(invited). Jordan Kodner. "The Resource Problem in Computational Linguistics." Les langues africaines à l’épreuve de la modernité. Hybrid: Stony Brook University Dept. of Africana Studies and CERDOTOLA, Yaoundé, Cameroon.
(invited). Jordan Kodner. "LLMs and linguistics: Use them for what they are, and don't try to make them what they aren't." 13th Annual Marshall M. Weinberg Symposium "What do Large Language Models Tell Us About Human anguage?". Weinberg Institute for Cognitive Science, University of Michigan. Ann Arbor, MI. (slides)
Jordan Kodner and Emmett Davitian. ""Irregulars" in the Italian Past Participles." LSA 2025. Philadelphia.
Jordan Kodner and Sarah Payne. "Formally defining the learning setting for child language acquisition." Computational Models of Learnability and Acquisition of Morphology and Phonology special session at LSA 2025. Philadelphia.

2024

(invited). Jordan Kodner. "Child Language Acquisition and a Mechanistic View of Language Change." CUNY Graduate Center Linguistics Colloquium. New York.
(keynote). Jordan Kodner. "Child Language Acquisition and a Mechanistic View of Language Change." The 25th Conference on Dichronic Generative Syntax Conference. Mannheim, Germany. (slides)
(invited). Jordan Kodner. "Is it Language or Task Design? Reinterpreting language models' recent successes in morphology and syntax learning." OFAI Lecture Series. Austrian Research Institute for Artificial Intelligence (OFAI).
(invited). Jordan Kodner. "The Language or the Task Design? Re-Evaluating Morphological Inflection Tasks." Computational Linguistics Colloquium Series. University of Toronto. (slides)
(invited). Jordan Kodner. "'Elsewhere Reversal' in Tehrani Armenian: A Case of Language Acquisition-Driven Language Change." Lectures on Iranian Linguistics in Arizona (LILA). University of Arizona.