Papers by Kate McCurdy
Inflecting When There’s No Majority: Limitations of Encoder-Decoder Neural Networks as Cognitive Models for German Plurals (2020.acl-main)
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| Challenge: | Encoder-decoder models can be used to generalize to inflectional morphology and generalize new words, but they fail on tasks like German number inflection, where infrequent suffixes like /-s/ can still be productively generalized. |
| Approach: | They propose to use a dataset to collect data from German speakers to examine whether ED models can generalize the most frequently produced plural class. |
| Outcome: | The proposed model does not show human-like variability or ‘regular’ extension of other plural markers. |
Systematicity between Forms and Meanings across Languages Supports Efficient Communication (2026.acl-long)
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| Challenge: | Languages vary in how meanings map to word forms, but this theory does not account for systematic relations within word forms. |
| Approach: | They propose a model that measures the learnability of meaning-to-form mappings by inverse of simplicity. |
| Outcome: | The proposed model captures fine-grained regularities in linguistic form, allowing better discrimination between attested and unattested systems. |
Toward Compositional Behavior in Neural Models: A Survey of Current Views (2024.emnlp-main)
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| Challenge: | Compositionality is a core property of natural language, and it is regarded as a key goal for modern NLP systems. |
| Approach: | They propose a conceptual framework to address compositionality in NLP . they propose to use this framework to survey researchers active in this area . |
| Outcome: | The proposed framework finds consensus on key points and suggests that scale alone is unlikely to achieve the desired behavior. |