I’m a postdoctoral researcher working with Michael Hahn. I recently completed my PhD in the School of Informatics, University of Edinburgh. I’ve also studied at McGill and Potsdam, worked as a researcher at Microsoft and Harvard, and founded a computational linguistics engineering team in the language-learning startup Babbel.
I’m a computational linguist with psycholinguistic training and broad interests in language and cognition. My research combines computational modeling and behavioral experiments to address fundamental scientific questions about how human language is structured and processed. I focus particularly on generalization and how it works in inflectional morphology.
I recently led a survey with Paul Smolensky and colleagues at Microsoft Research to see what researchers think about compositionality in neural network models. Data collection is complete, but you can still take the survey yourself and check our paper to see how your views fit with the field as a whole!
Morphological generalization, or the task of mapping an unknown word (such as a novel noun Raun) to an inflected form (such as the plural Rauns), has historically proven a contested topic within computational linguistics and cognitive science, e.g. within the past tense debate (Rumelhart and McClelland, 1986; Pinker and Prince, 1988; Seidenberg and Plaut, 2014). Marcus et al. (1995) identified German plural inflection as a key challenge domain to evaluate two competing accounts of morphological generalization: a rule generation view focused on linguistic features of input words, and a type frequency view focused on the distribution of output inflected forms, thought to reflect more domain-general cognitive processes. More recent behavioral and computational research developments support a new view based on predictability, which integrates both input and output distributions. My research uses these methodological innovations to revisit a core dispute of the past tense debate: how do German speakers generalize plural inflection, and can computational learners generalize similarly? This dissertation evaluates the rule generation, type frequency, and predictability accounts of morphological generalization in a series of behavioral and computational experiments with the stimuli developed by Marcus et al.. I assess predictions for three aspects of German plural generalization: distribution of infrequent plural classes, influence of grammatical gender, and within-item variability. Overall, I find that speaker behavior is best characterized as frequency-matching to a phonologically-conditioned lexical distribution. This result does not support the rule generation view, and qualifies the predictability view: speakers use some, but not all available information to reduce uncertainty in morphological generalization. Neural and symbolic model predictions are typically overconfident relative to speakers; simple Bayesian models show somewhat higher speaker-like variability and accuracy. All computational models are outperformed by a static phonologically-conditioned lexical baseline, suggesting these models have not learned the selective feature preferences that inform speaker generalization.
@phdthesis{mccurdy_rules_2024,title={Rules, frequency, and predictability in morphological generalization: behavioral and computational evidence from the {German} plural system},shorttitle={Rules, frequency, and predictability in morphological generalization},url={https://era.ed.ac.uk/handle/1842/41429},language={en},urldate={2024-11-19},author={McCurdy, Kate},school={School of Informatics, University of Edinburgh},year={2024},file={Full Text PDF:C\:\\Users\\Kate\\Zotero\\storage\\DFQ49QZA\\McCurdy and McCurdy - 2024 - Rules, frequency, and predictability in morphologi.pdf:application/pdf}}
Lossy Context Surprisal Predicts Task-Dependent Patterns in Relative Clause Processing
Kate McCurdy, and Michael Hahn
In Proceedings of the 28th Conference on Computational Natural Language Learning, 2024
English relative clauses are a critical test case for theories of syntactic processing. Expectation- and memory-based accounts make opposing predictions, and behavioral experiments have found mixed results. We present a technical extension of Lossy Context Surprisal (LCS) and use it to model relative clause processing in three behavioral experiments. LCS predicts key results at distinct retention rates, showing that task-dependent memory demands can account for discrepant behavioral patterns in the literature.
@inproceedings{mccurdy_lossy_2024,address={Miami, FL, USA},title={Lossy {Context} {Surprisal} {Predicts} {Task}-{Dependent} {Patterns} in {Relative} {Clause} {Processing}},urldate={2024-11-20},booktitle={Proceedings of the 28th {Conference} on {Computational} {Natural} {Language} {Learning}},publisher={Association for Computational Linguistics},author={McCurdy, Kate and Hahn, Michael},editor={Barak, Libby and Alikhani, Malihe},year={2024},pages={36--45},file={Full Text PDF:C\:\\Users\\Kate\\Zotero\\storage\\3NYFTL2B\\McCurdy and Hahn - 2024 - Lossy Context Surprisal Predicts Task-Dependent Pa.pdf:application/pdf}}
Toward Compositional Behavior in Neural Models: A Survey of Current Views
Kate McCurdy, Paul Soulos, Paul Smolensky, and 2 more authors
In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024
Compositionality is a core property of natural language, and compositional behavior (CB) is a crucial goal for modern NLP systems. The research literature, however, includes conflicting perspectives on how CB should be defined, evaluated, and achieved. We propose a conceptual framework to address these questions and survey researchers active in this area.We find consensus on several key points. Researchers broadly accept our proposed definition of CB, agree that it is not solved by current models, and doubt that scale alone will achieve the target behavior. In other areas, we find the field is split on how to move forward, identifying diverse opportunities for future research.
@inproceedings{mccurdy_toward_2024,address={Miami, Florida, USA},title={Toward {Compositional} {Behavior} in {Neural} {Models}: {A} {Survey} of {Current} {Views}},shorttitle={Toward {Compositional} {Behavior} in {Neural} {Models}},urldate={2024-11-20},booktitle={Proceedings of the 2024 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing}},publisher={Association for Computational Linguistics},author={McCurdy, Kate and Soulos, Paul and Smolensky, Paul and Fernandez, Roland and Gao, Jianfeng},editor={Al-Onaizan, Yaser and Bansal, Mohit and Chen, Yun-Nung},year={2024},pages={9323--9339},file={Full Text PDF:C\:\\Users\\Kate\\Zotero\\storage\\4PQEFYE4\\McCurdy et al. - 2024 - Toward Compositional Behavior in Neural Models A .pdf:application/pdf},}
