Intermediate layer knowledge distillation (KD) can improve the standard KD technique
(which only targets the output of teacher and student models) especially over large pre …

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 …

Data Augmentation (DA) is frequently used to automatically provide additional training data
without extra human annotation. However, data augmentation may introduce noisy data that …

In this paper, we propose SRL4NLP, a new approach for data augmentation by drawing an
analogy between image and text processing: Super-resolution learning. This method is …

In this study, we investigate the potential of language models (LMs) in aiding patients
experiencing anomia, a difficulty identifying the names of items. Identifying the intended …

In extreme multi-label text classification (XMC), labels usually follow a long-tailed
distribution, where most labels only contain a small number of documents and limit the …

Knowledge distillation (KD) is an efficient framework for compressing large-scale pre-trained
language models. Recent years have seen a surge of research aiming to improve KD by …

Data augmentation technologies, which can overcome the expensive and time-consuming
issue of high-quality labeled data generation in semantic textual similarity (STS) tasks, use …

As deep learning applications, especially programs of computer vision, are increasingly
deployed in our lives, we have to think more urgently about the security of these …

Many manufacturing industries in third-world countries are still in need of process systems
improvement in order to increase productivity. Some manufacturing plants are encountering …