Scholarly Works in Linguistics & African Languages

Permanent URI for this collectionhttps://repository.ibadanedu.com/handle/123456789/347

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Author: search.filters.author.Odoje, C. O.Subject: search.filters.subject.Statistical Machine Translation

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    Human Evaluation of Yorùbá-English Google Translation
    (Creative Common Attribution-Non-Commercial 4.0 International, 2016) Odoje, C. O.
    The task of Machine Translation is not just about translating the text of a language to another but also its evaluation so as to monitor its improvement particularly in fluency, accuracy and efficiency. However, the only available free machine translation on Yoruba-English is “Google Translate” which has been observed to be grossly inadequate. This paper therefore examines translations done by Google Translate as against human translation in order to investigate why machine translation applications make some errors while translating human natural language. There are many matrix evaluators to do this. This paper adopts human evaluation also known as manual evaluation which is considered to be more efficient, but costly. The paper adopts Ibadan and Akungba Structured Sentence Paradigm to evaluate the translators (Google Translate and human). The translations were sent to twenty human evaluators out of which only eleven responded. The responses were subjected to statistical analysis. Findings show that human translation fares better in terms of accuracy and fluency which are informed by the quality and the quantity of training data. This paper suggests that more data, especially literary texts, should be acquired to train the translator for general efficiency and fluency.
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    Exploring the Challenges of SMT Project for Resource Scarce Language in Africa: A Case Study of English-Yoruba Language Machine Translation
    (Research in African Languages & Linguistics, 2013) Odoje, C. O.; Akinola S. O.
    The challenges of Machine Translation (MT) and in particular Statistical Machine Translation (SMT) have been explored and categorized. But little is known about African languages which are said to be resource scare languages. Hence, this paper explored the challenges of SMT for African languages using English-Yoriiba MT as case study. Fagunwa's books and its English translated equivalents were used as corpus and MoSes was used as the language toolkit. In the first part series of this study, we observed that the challenges of Yoriiba SMT were inexhaustible. However, the challenges of Africa SMT were categorized into two: technical and sociocultural peculiarities.