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

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 …

Pre-trained language models (LMs) store knowledge in their parameters and can generate
informative responses when used in conversational systems. However, LMs suffer from the …

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 …

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

Transformer based language models such as BERT have been widely applied to many
domains through model pretraining and fine tuning. However, in low-resource scenarios …

Pretraining domain-specific language models remains an important challenge which limits
their applicability in various areas such as agriculture. This paper investigates the …

Data augmentation is an effective approach to tackle over-fitting. Many previous works have
proposed different data augmentations strategies for NLP, such as noise injection, word …

Data augmentation is a commonly-used technique to avoid over-fitting in deep learning.
However, the mechanism behind effective data augmentation methods is unclear. To …