Bridging Language Barriers in Education with Statistical Machine Translation

Machine Translation (MT) enables real-time language translation, enhancement of language processing efficiency, and accuracy. The world is becoming more and more globalized in terms of education and this is where scalable means of fostering multilingual education are required today particularly where the requirements of different languages are high. The classical machine translation algorithm cannot translate the instructional information because of the contextual accuracy, idiosyncrasy of the language used, and the dynamic learning conditions. The paper suggests a new solution that will be a combination of Meta Reinforcement Learning and Uncertainty Estimation (Meta-Uncertainty RL) to resolve the issues. The combination of meta-learning in quick adaptation to a wide range of languages and the use of uncertainty to make decisions also enables the proposed approach to adapt the system to various education settings and languages in response to a variety of learning environments to support high-quality translation. The accuracy of translation and the knowledge of the context are enhanced with the ongoing policy adjustment without retraining. The dataset of multilingual IWSLT 2017 was used to test the model. This was improved by better contextualization of vague translations and managing unclear translations. The suggested approach will kill a major requirement in bilingual education by offering a scalable and flexible solution on machine translation in real-time in foreign classes.

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