Abstract Arabic Machine Translation (MT) has gained considerable attention from the
research community due to the widespread use of Arabic as one of the world's major …

As Large Language Models (LLMs) have achieved significant success in handling multi-
modal tasks such as text, images, videos, and sounds, particularly showcasing emergent …

We investigate a data-driven single-period inventory management problem with uncertain
demand, where large amounts of textual online reviews and historical data are accessible …

Background Non-English speaking researchers may find it difficult to write articles in English
and may be tempted to use machine translators (MTs) to facilitate their task. We compared …

Accurately estimating daily tourism volumes is crucial for optimizing operational strategies
and enhancing visitor experiences at tourist destinations. In this study, we leverage historical …

Advances in neural machine translation utilizing pretrained language models (PLMs) have
shown promise in improving the translation quality between diverse languages. However …

The attention mechanism performs well for the Neural Machine Translation (NMT) task, but
heavily depends on the context vectors generated by the attention network to predict target …

An English-Bengali machine translation (MT) application can convert an English text into a
corresponding Bengali translation. To build a better model for this task, we can optimize …

Overt and Covert errors are categorized as translation assessments that focus on functional
match of lexical items. This study investigates functional equivalence (overt error) in …

Overcoming language barriers has long been a significant barrier to global communication
and cooperation. With the advancement of AI technology, a new era of simple, effective, and …