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Found 6 papers, 3 papers with code
Multi-News+: Cost-efficient Dataset Cleansing via LLM-based Data Annotation
The quality of the dataset is crucial for ensuring optimal performance and reliability of downstream task models.
Enhancing Effectiveness and Robustness in a Low-Resource Regime via Decision-Boundary-aware Data Augmentation
Inspired by recent studies on decision boundaries, this paper proposes a decision-boundary-aware data augmentation strategy to enhance robustness using pretrained language models.
GPTs Are Multilingual Annotators for Sequence Generation Tasks
However, the conventional approach of data annotation through crowdsourcing is both time-consuming and expensive.
SoftEDA: Rethinking Rule-Based Data Augmentation with Soft Labels
Rule-based text data augmentation is widely used for NLP tasks due to its simplicity.
AutoAugment Is What You Need: Enhancing Rule-based Augmentation Methods in Low-resource Regimes
Text data augmentation is a complex problem due to the discrete nature of sentences.
Restoring and Mining the Records of the Joseon Dynasty via Neural Language Modeling and Machine Translation
Understanding voluminous historical records provides clues on the past in various aspects, such as social and political issues and even natural science facts.
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