This paper examined the fundamental of educational reform to national development with relevance to Nigerian educational system. The rational for educational reform to national development was well highlighted and the needs for educational reform such as to improve on the standard, future expectations, exogenous factors, achievement inclined and creativity were properly expatiated. Planning for reform in educational structure, curriculum and methods as well as management of educational reforms which are the backbone to effective and efficient educational reform were well elucidated. The Federal Government of Nigeria had envisaged and prepared for reform in education through the provision of goal oriented national policy on education. Basis for evaluation of educational reform which is a necessity as well as procedure and levels of evaluation are fully highlighted and discussed. Factors militating against effective educational reform in Nigeria were identified and explained. The paper itemized some recommendations toward sustainable educational reform in Nigeria.

Recent advances in the pre-training of language models leverage large-scale datasets to create multilingual models. However, lowresource languages are mostly left out in these datasets. This is primarily because many widely spoken languages are not well represented on the web and therefore excluded from the large-scale crawls used to create datasets. Furthermore, downstream users of these models are restricted to the selection of languages originally chosen for pre-training. This work investigates how to optimally leverage existing pre-trained models to create low-resource translation systems for 16 African languages. We focus on two questions: 1) How can pretrained models be used for languages not included in the initial pre-training? and 2) How can the resulting translation models effectively transfer to new domains? To answer these questions, we create a new African news corpus covering 16 languages, of which eight languages are not part of any existing evaluation dataset. We demonstrate that the most effective strategy for transferring both to additional languages and to additional domains is to finetune large pre-trained models on small quantities of high-quality translation data.

Recent advances in the pre-training of language models leverage large-scale datasets to create multilingual models. However, low-resource languages are mostly left out in these datasets. This is primarily because many widely spoken languages are not well represented on the web and therefore excluded from the large-scale crawls used to create datasets. Furthermore, downstream users of these models are restricted to the selection of languages originally chosen for pre-training. This work investigates how to optimally leverage existing pre-trained models to create low-resource translation systems for 16 African languages. We focus on two questions: 1) How can pre-trained models be used for languages not included in the initial pre-training? and 2) How can the resulting translation models effectively transfer to new domains? To answer these questions, we create a new African news corpus covering 16 languages, of which eight languages are not part of any existing evaluation dataset. We demonstrate that the most effective strategy for transferring both to additional languages and to additional domains is to fine-tune large pre-trained models on small quantities of highquality translation data.