Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …

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 …

In this work, we provide a survey of active learning (AL) for its applications in natural
language processing (NLP). In addition to a fine-grained categorization of query strategies …

Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within
the field of NLP, aimed at addressing limitations in existing frameworks while aligning with …

With proliferation of user generated contents in social media platforms, establishing
mechanisms to automatically identify toxic and abusive content becomes a prime concern …

GPT-3 is a large-scale natural language model developed by OpenAI that can perform many
different tasks, including topic classification. Although researchers claim that it requires only …

GPT (Generative Pre-trained Transformer) represents advanced language models that have
significantly reshaped the academic writing landscape. These sophisticated language …

We present Mask-then-Fill, a flexible and effective data augmentation framework for event
extraction. Our approach allows for more flexible manipulation of text and thus can generate …

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 …