DAGAM: Data Augmentation with Generation And Modification

Jo, Byeong-Cheol; Heo, Tak-Sung; Park, Yeongjoon; Yoo, Yongmin; Cho, Won Ik; Kim, Kyungsun

doi:10.48550/arxiv.2204.02633

Cited by 1 publication

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“…Wu et al [22] proposed a text smoothing method that uses BERT to encode one-hot representations into smoothed and interpolated representations for data augmentation. Jo et al [23] proposed the DAGAM method, which uses three sentences with the same label as input and employs the T5 generation model [24] to generate augmented sentences. Liu et al [25] proposed the SRAFBN model, which uses a self-attention mechanism to extract the key information in images so that the generated images can be of a higher quality.…”

Section: Data Augmentationmentioning

confidence: 99%

Application of Generative Adversarial Networks and Shapley Algorithm Based on Easy Data Augmentation for Imbalanced Text Data

Huang

2022

Applied Sciences

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Imbalanced data constitute an extensively studied problem in the field of machine learning classification because they result in poor training outcomes. Data augmentation is a method for increasing minority class diversity. In the field of text data augmentation, easy data augmentation (EDA) is used to generate additional data that would otherwise lack diversity and exhibit monotonic sentence patterns. Generative adversarial network (GAN) models can generate diverse sentence patterns by using the probability corresponding to each word in a language model. Therefore, hybrid EDA and GAN models can generate highly diverse and appropriate sentence patterns. This study proposes a hybrid framework that employs a generative adversarial network and Shapley algorithm based on easy data augmentation (HEGS) to improve classification performance. The experimental results reveal that the HEGS framework can generate highly diverse training sentences to form balanced text data and improve text classification performance for minority classes.

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Section: Data Augmentationmentioning

confidence: 99%

Application of Generative Adversarial Networks and Shapley Algorithm Based on Easy Data Augmentation for Imbalanced Text Data

Huang

2022

Applied Sciences

View full text Add to dashboard Cite

show abstract

DAGAM: Data Augmentation with Generation And Modification

Cited by 1 publication

References 0 publications

Application of Generative Adversarial Networks and Shapley Algorithm Based on Easy Data Augmentation for Imbalanced Text Data

Application of Generative Adversarial Networks and Shapley Algorithm Based on Easy Data Augmentation for Imbalanced Text Data

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