An evaluation of machine translation systems for Hausa–English language pairs in Kano
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Background of the Study
Machine translation (MT) systems have evolved as pivotal tools in bridging language barriers, particularly in multilingual societies like Nigeria. In Kano, where Hausa and English co-exist in various domains such as education, administration, and commerce, effective translation systems can foster better communication and integration. Recent developments in neural machine translation have improved translation accuracy for well-resourced language pairs; however, Hausa remains underrepresented in digital linguistic resources (Abubakar, 2023). This study investigates the efficacy of MT systems specifically designed for Hausa–English language pairs, assessing their ability to capture nuanced meanings, idiomatic expressions, and contextual subtleties. The research focuses on evaluating existing systems, understanding their limitations, and proposing improvements based on local linguistic data. Given Kano's unique sociolinguistic landscape, where language use often reflects cultural and contextual influences, enhancing MT systems for Hausa is essential for promoting literacy and access to digital content. Emerging research indicates that incorporating localized data significantly improves translation outputs (Sani, 2024). Thus, the study aims to contribute to the development of more accurate and culturally sensitive MT systems for Hausa–English translation.
Statement of the Problem
Despite progress in MT technology, the Hausa–English language pair faces significant challenges due to the paucity of quality parallel corpora and the complexities of idiomatic expressions in Hausa. Existing systems often produce translations that lack cultural context and accuracy (Abubakar, 2023; Sani, 2024). This inadequacy affects communication in formal and informal settings, impeding educational and administrative functions in Kano. Consequently, there is a critical need for evaluating and refining MT systems to better serve the linguistic demands of Hausa speakers.
Objectives of the Study
- To evaluate the translation accuracy of current MT systems for Hausa–English pairs.
- To identify linguistic challenges specific to Hausa that affect machine translation quality.
- To propose enhancements for improving MT performance using localized corpora.
Research Questions
- How accurate are current MT systems in translating Hausa to English and vice versa?
- What are the key linguistic challenges in Hausa that hinder translation accuracy?
- How can localized linguistic data improve MT systems for Hausa–English translations?
Significance of the Study
This study will provide valuable insights into the performance of MT systems for the Hausa–English language pair, contributing to improved translation technologies that support cross-cultural communication in Kano. The findings will assist developers, linguists, and policymakers in enhancing digital translation tools and promoting inclusive access to information.
Scope and Limitations of the Study
This study is confined to evaluating machine translation systems for Hausa–English language pairs in Kano. It does not extend to other language pairs or geographical regions.
Definitions of Terms
- Machine Translation (MT): The automated process of converting text from one language to another.
- Parallel Corpus: A collection of texts in two or more languages used for translation model training.
- Neural Machine Translation: An advanced MT approach that utilizes neural networks for improved translation quality.
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