Papers by Hyunjoon Cheon

2 papers
Self-Training using Rules of Grammar for Few-Shot NLU (2021.findings-emnlp)

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Challenge: Existing methods for learning natural language understanding are limited in low-resource settings.
Approach: They propose to use rules of grammar to construct and expand rules of grammatical structure of data without human involvement.
Outcome: The proposed approach outperforms state-of-the-art methods in three benchmark datasets.
GDA: Grammar-based Data Augmentation for Text Classification using Slot Information (2023.findings-emnlp)

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Challenge: Recent studies suggest data augmentation approaches to resolve the low-resource problem in natural language processing tasks.
Approach: They propose to use slot information to augment sentences using a set of injective relations between a sentence’s semantics and its syntactical structure to augment the dataset.
Outcome: The proposed approach outperforms all other data augmentation methods by 19.38%.

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