Papers by Ray Groshan

1 papers
Is linguistically-motivated data augmentation worth it? (2025.acl-long)

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Challenge: Data augmentation is a widely-used technique for addressing data scarcity.
Approach: They compare linguistically-motivated and linguisticly-naive data augmentation strategies for two low-resource languages with different morphological properties.
Outcome: The proposed methods produce synthetic data that follows all linguistic constraints, but they require linguistic expertise and are more difficult to implement.

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