Papers by Jordan Kodner
Bootstrapping Transliteration with Constrained Discovery for Low-Resource Languages (D18-1)
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| Challenge: | Existing approaches to transliteration generation require a large number of training examples. |
| Approach: | They propose a bootstrapping algorithm that uses constrained discovery to improve generation . they show that the model can be used with as few as 500 training examples . |
| Outcome: | The proposed method improves on nine languages written in a unique script. |
Exploring Linguistic Probes for Morphological Inflection (2023.emnlp-main)
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| Challenge: | morphological inflection models typically employ language-independent data splitting algorithms. |
| Approach: | They propose language-specific probes to test aspects of morphological generalization . they use three morphology-distinct languages to test their generalization abilities . |
| Outcome: | The proposed language-specific probes are used to test morphological generalization abilities on three distinct languages. |
Modeling Morphological Typology for Unsupervised Learning of Language Morphology (2020.acl-main)
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| Challenge: | Existing approaches to morphological analysis relied on hand-built rules to identify word-internal structures. |
| Approach: | They propose a language-independent model for fully unsupervised morphological analysis that exploits a universal framework leveraging morphology. |
| Outcome: | The proposed model outperforms existing systems on nine typologically and genetically diverse languages and shows superior performance over leading systems. |
A Cautious Generalization Goes a Long Way: Learning Morphophonological Rules (2023.acl-long)
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| Challenge: | Explicit linguistic knowledge encoded by rule-based morphological analyzers is expensive and non-trivial . creating such resources is tedious and requires additional efforts to extract human-interpretable patterns from them. |
| Approach: | They propose a method for automatically learning morphophonological rules of Arabic from a corpus. |
| Outcome: | The proposed approach produces a set of generalizable rules from a dataset. |
Morphological Inflection: A Reality Check (2023.acl-long)
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| Challenge: | Morphological inflection is a popular task in sub-word NLP with practical and cognitive applications. |
| Approach: | They propose new methods to analyze data sets and evaluate their generalization abilities to better reflect likely use-cases. |
| Outcome: | The proposed methods improve generalizability and reliability of results and improve generalization abilities. |
Morphological Segmentation for Low Resource Languages (2020.lrec-1)
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Justin Mott, Ann Bies, Stephanie Strassel, Jordan Kodner, Caitlin Richter, Hongzhi Xu, Mitchell Marcus
| Challenge: | a new corpus of annotated morphological data is described for the DARPA LORELEI Program . the data is annotating 9 low resource languages and root information for 7 of the languages . |
| Approach: | This paper describes a new morphology resource created by Linguistic Data Consortium and the University of Pennsylvania for the DARPA LORELEI Program. |
| Outcome: | The annotated corpus provides a gold standard for unsupervised morphological segmenters and analyzers . the language-specific annotation guidelines were language-independent, but included morphology paradigms and other specifications. |
LLMs can Perform Multi-Dimensional Analytic Writing Assessments: A Case Study of L2 Graduate-Level Academic English Writing (2025.acl-long)
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| Challenge: | a growing number of studies have indicated the general usefulness of LLMs for automated writing assessments. |
| Approach: | They propose a framework that evaluates LLMs' ability to provide scores and comments based on multiple assessment criteria. |
| Outcome: | The proposed framework is interpretable, cost-efficient, scalable, and reproducible . it is compared to existing methods that rely on manual judgments . |
Lemmas Matter, But Not Like That: Predictors of Lemma-Based Generalization in Morphological Inflection (2025.findings-acl)
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| Challenge: | Recent work suggests that lemma overlap drives model performance on morphological inflection tasks, but the impact of lemmm overlap is debated. |
| Approach: | They propose a novel algorithm to investigate predictors of accuracy on seen and unseen lemmas by combining the number of lema in train with the number in train. |
| Outcome: | The proposed algorithm shows that the number of lemmas in train has a stronger effect on accuracy on unseen than seen lemmes. |
A Framework for Representing Language Acquisition in a Population Setting (P18-1)
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| Challenge: | Existing approaches to model language acquisition and social structure are ineffective because nobody can travel back in time or fit entire natural environments into a lab. |
| Approach: | They propose a new analytic framework which combines previous network models' ability to capture realistic social structure with more elegant computational properties. |
| Outcome: | The proposed framework is able to capture real social structure and integrate with existing models while being modular and extensible. |
Overestimation of Syntactic Representation in Neural Language Models (2020.acl-main)
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| Challenge: | Several testing methodologies have been developed to probe models’ syntactic representations. |
| Approach: | They propose a method to determine syntactic structure by training a model on strings generated according to a template and testing its ability to distinguish between similar ones with different syntax. |
| Outcome: | The proposed method reproduces positive results with two non-syntactic baseline language models: an n-gram model and an LSTM model trained on scrambled inputs. |