Enhancing Machine Translation: Neural and Hybrid MT engines Directing Strategies

    Ayman Mohamed Nasr Eldien El-Esery
    Keywords: Machine Translation, direct MT engines, Neural MT engines, Hybrid MT engines, MT strategies, MT electronic metric. ,

    Abstract

    Abstract
    MT direct engines are used excessively by language learners and in translation classrooms. Such MT
    engines don’t allow human interference while processing the translation task. Thus, applying some
    translation strategies during the preparation stage is essential in directing MT engines. The researcher of
    the present study trained a group of EFL learners in Qassim University to apply some strategies on Arabic
    source texts before submitting them to MT engines. The researcher used an electronic metric “BLEU” to
    assess the effect of the proposed strategies in directing MT engines toward accuracy. Furthermore, the
    responses of two neural MT engines (Google Translate and Systran) and four Hybrid MT engines (Yandix,
    Reverso, Collins, and Microsoft) to the premodified text (modified through the proposed strategies)were
    compared. Results showed that the translation strategies are effective and direct the MT engines toward
    accuracy. The pre-translation strategies led to 37.8 enhancement in MT translation. As for comparing the
    response of the six MT engines, results indicated that all the six MT engines responded effectively to the
    modified text and Neural Google Translate had the highest response of all.

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