Recent advancements in deep learning architecture have increased its utility in real-life
applications. Deep learning models require a large amount of data to train the model. In …

The process of creating high-quality labeled data is crucial for training machine-learning
models, but it can be a time-consuming and labor-intensive process. Moreover, manual …

Text augmentation is a popular technique in natural language processing (NLP) that has
been shown to improve the performance of various downstream tasks. The goal of text …

In synonym replacement-based data augmentation techniques for natural language
processing tasks, words in a sentence are often sampled randomly with equal probability. In …

The engineering geology report serves as a comprehensive portrayal of the geological
conditions and information within a surveyed region, making it highly valuable for extracting …

Category text generation receives considerable attentions since it is beneficial for various
natural language processing tasks. Recently, the generative adversarial network (GAN) has …

Abstract Counterfactually-Augmented Data (CAD)–minimal editing of sentences to flip the
corresponding labels–has the potential to improve the Out-Of-Distribution (OOD) …

Text augmentation is an effective technique for addressing the problem of insufficient data in
natural language processing. However, existing text augmentation methods tend to focus on …

For geoscience text, rich domain corpora have become the basis of improving the model
performance in word segmentation. However, the lack of domain-specific corpus with …

Text classification is a very common and important task in Natural Language Processing. In
many domains and real-world settings, a few labeled instances are the only resource …