Papers by Tanmay Surana

1 papers
CASSI: Contextual and Semantic Structure-based Interpolation Augmentation for Low-Resource NER (2023.findings-emnlp)

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Challenge: Existing methods for text augmentation suffer from annotation corruption for token-level tasks like NER.
Approach: They propose a novel augmentation scheme that generates high-quality contextually diverse augmentations while avoiding annotation corruption.
Outcome: The proposed scheme outperforms existing methods at multiple low resource levels, in multiple languages, and for noisy and clean text.

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