Abstract Machine translation (MT) is an important sub-field of natural language processing
that aims to translate natural languages using computers. In recent years, end-to-end neural …

AI is widely thought to be poised to transform business, yet current perceptions of the scope
of this transformation may be myopic. Recent progress in natural language processing …

Owing to rich spectral and spatial information, hyperspectral image (HSI) can be utilized for
finely classifying different land covers. With the emergence of deep learning techniques …

Referring video object segmentation (RVOS) is a challenging language-guided video
grounding task, which requires comprehensively understanding the semantic information of …

Automatic captioning of images is a task that combines the challenges of image analysis
and text generation. One important aspect of captioning is the notion of attention: how to …

Transformers have dominated empirical machine learning models of natural language
processing. In this paper, we introduce basic concepts of Transformers and present key …

Transformer-based models have brought a radical change to neural machine translation. A
key feature of the Transformer architecture is the so-called multi-head attention mechanism …

Long sequence time-series forecasting (LSTF) problems are widespread in the real world,
such as weather forecasting, stock market forecasting, and power resource management …

Since the early 1980s, the legal domain has shown a growing interest in Artificial
Intelligence approaches to tackle the increasing number of cases worldwide. TaSbeeb is a …

Incorporating hierarchical structures like constituency trees has been shown to be effective
for various natural language processing (NLP) tasks. However, it is evident that state-of-the …