As an effective strategy, data augmentation (DA) alleviates data scarcity scenarios where
deep learning techniques may fail. It is widely applied in computer vision then introduced to …

Data augmentation has recently seen increased interest in NLP due to more work in low-
resource domains, new tasks, and the popularity of large-scale neural networks that require …

Large-scale language models such as GPT-3 are excellent few-shot learners, allowing them
to be controlled via natural text prompts. Recent studies report that prompt-based direct …

Automatic modulation classification is an essential and challenging topic in the development
of cognitive radios, and it is the cornerstone of adaptive modulation and demodulation …

Large language models (LLMs) can be used to generate text data for training and evaluating
other models. However, creating high-quality datasets with LLMs can be challenging. In this …

Simple yet effective data augmentation techniques have been proposed for sentence-level
and sentence-pair natural language processing tasks. Inspired by these efforts, we design …

In this paper, we explore the slot tagging with only a few labeled support sentences (aka few-
shot). Few-shot slot tagging faces a unique challenge compared to the other few-shot …

Mixup is the latest data augmentation technique that linearly interpolates input examples
and the corresponding labels. It has shown strong effectiveness in image classification by …

In recent years, fostered by deep learning technologies and by the high demand for
conversational AI, various approaches have been proposed that address the capacity to …

Most previous methods for text data augmentation are limited to simple tasks and weak
baselines. We explore data augmentation on hard tasks (ie, few-shot natural language …