Performance of Transformer-Based Models in Dholuo-Swahili Translation for Low-Resourced Language

Neural Machine Translation in Low-Resourced Languages: Case of Dholuo-Swahili Translation

Pre-Trained Transformer-Based Models for Text Classification Using Low-Resourced Ewe Language

Despite a few attempts to automatically crawl Ewe text from online news portals and magazines, the A

Low-Resourced Machine Translation for Senegalese Wolof Language

Natural Language Processing (NLP) research has made great advancements in recent years with major br

On Optimal Transformer Depth for Low-Resource Language Translation

Transformers have shown great promise as an approach to Neural Machine Translation (NMT) for low-resource languages. However, at the same time, transformer models remain difficult to optimize and require careful tuning of hyper-parameters to be useful in this setti

Crowdsourced Phrase-Based Tokenization for Low-Resourced Neural Machine Translation: The Case of Fon Language

Building effective neural machine translation (NMT) models for very low-resourced and morphologically rich African indigenous languages is an open challenge. Besides the issue of finding available resources for them, a lot of work is put into preprocessing and toke

Exploring Graph-based Transformer Encoder for Low-Resource Neural Machine Translation

The Transformer is commonly used in Neural Machine Translation (NMT), but it faces issues with over-