no code implementations • 15 Apr 2024 • Juhwan Choi, Jungmin Yun, Kyohoon Jin, Youngbin Kim
The quality of the dataset is crucial for ensuring optimal performance and reliability of downstream task models.
no code implementations • 22 Mar 2024 • Kyohoon Jin, Junho Lee, Juhwan Choi, Sangmin Song, Youngbin Kim
Inspired by recent studies on decision boundaries, this paper proposes a decision-boundary-aware data augmentation strategy to enhance robustness using pretrained language models.
1 code implementation • 8 Feb 2024 • Juhwan Choi, Eunju Lee, Kyohoon Jin, Youngbin Kim
However, the conventional approach of data annotation through crowdsourcing is both time-consuming and expensive.
1 code implementation • 8 Feb 2024 • Juhwan Choi, Kyohoon Jin, Junho Lee, Sangmin Song, Youngbin Kim
Rule-based text data augmentation is widely used for NLP tasks due to its simplicity.
1 code implementation • 8 Feb 2024 • Juhwan Choi, Kyohoon Jin, Junho Lee, Sangmin Song, Youngbin Kim
Text data augmentation is a complex problem due to the discrete nature of sentences.
no code implementations • NAACL 2021 • Kyeongpil Kang, Kyohoon Jin, Soyoung Yang, Sujin Jang, Jaegul Choo, Youngbin Kim
Understanding voluminous historical records provides clues on the past in various aspects, such as social and political issues and even natural science facts.