Data augmentation, the artificial creation of training data for machine learning by
transformations, is a widely studied research field across machine learning disciplines …

Deep learning algorithms have exhibited impressive performance across various computer
vision tasks; however, the challenge of overfitting persists, especially when dealing with …

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 …

NLP has achieved great progress in the past decade through the use of neural models and
large labeled datasets. The dependence on abundant data prevents NLP models from being …

Data Augmentation (DA) methods–a family of techniques designed for synthetic generation
of training data–have shown remarkable results in various Deep Learning and Machine …

We present CyBERT, a domain-specific Bidirectional Encoder Representations from
Transformers (BERT) model, fine-tuned with a large corpus of textual cybersecurity data …

Since empathy plays a crucial role in increasing social bonding between people, many
studies have designed their own dialogue agents to be empathetic using the well …

This paper explores new frontiers in agricultural natural language processing (NLP) by
investigating the effectiveness of food-related text corpora for pretraining transformer-based …

Cyber-defense systems are being developed to automatically ingest Cyber Threat
Intelligence (CTI) that contains semi-structured data and/or text to populate knowledge …

In the realm of Computational Social Science (CSS), practitioners often navigate complex,
low-resource domains and face the costly and time-intensive challenges of acquiring and …