Development and Evaluation of an English-to Igala Neural Machine Translation System using Deep Learning
Domaine:
natural language processingType de record:
datasetmodel
Créateur:
EmmFel
Éditeur:
Int
Hôte:
Low-resource languages face significant challenges in the digital age due to limited computational tools and data
resources. This study presents the development of a neural machine translation (NMT) system for English-to-Igala
translation using a Recurrent Neural Network (RNN) model. Igala is one of the under-resourced languages spoken in
Nigeria. A bilingual parallel corpus of 1000 English-Igala sentence pairs was compiled and preprocessed to train and
evaluate the system. The model achieved high translation accuracy as evidenced by BLEU scores above 0.5 on most test
sentences. This research provides a foundational step for the development of computational resources for Igala and supports
the broader goal of linguistic inclusivity in artificial intelligence.
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