The virtual-to-real paradigm, ie, training models on virtual data and then applying them to
solve real-world problems, has attracted more and more attention from various domains by …

A major factor in the recent success of large language models is the use of enormous and
ever-growing text datasets for unsupervised pre-training. However, naively training a model …

Data augmentation is an important component in the robustness evaluation of models in
natural language processing (NLP) and in enhancing the diversity of the data they are …

Data augmentation is an important procedure in deep learning. GAN-based data
augmentation can be utilized in many domains. For instance, in the credit card fraud domain …

State-of-the-art supervised NLP models achieve high accuracy but are also susceptible to
failures on inputs from low-data regimes, such as domains that are not represented in …

In the rapidly evolving field of machine learning (ML), data augmentation (DA) has emerged
as a pivotal technique for enhancing model performance by diversifying training examples …

Natural language processing (NLP) algorithms have become very successful, but they still
struggle when applied to out-of-distribution examples. In this paper we propose a …

Data augmentation has been an important ingredient for boosting performances of learned
models. Prior data augmentation methods for few-shot text classification have led to great …

Data-hungry deep neural networks have established themselves as the de facto standard for
many NLP tasks, including the traditional sequence tagging ones. Despite their state-of-the …

Data augmentation aims to enrich training samples for alleviating the overfitting issue in low-
resource or class-imbalanced situations. Traditional methods first devise task-specific …