Construction of Algorithms for English Learners to Read Foreign Literature Based on Machine Translation Technology

Abstract

The construction of algorithms for English learners to read foreign literature based on machine translation technology involves several key steps. Initially, the algorithm utilizes machine translation to convert the foreign text into English, ensuring accessibility for learners. Next, it employs natural language processing (NLP) techniques to enhance the readability and comprehension of the translated text, such as simplifying complex sentences, clarifying ambiguous phrases, and providing annotations or explanations for cultural or linguistic nuances. Additionally, the algorithm may incorporate adaptive learning mechanisms to personalize the reading experience, adjusting the difficulty level and content based on the learner's proficiency and preferences. This paper explores the development and implementation of algorithms designed to facilitate English learners' comprehension of foreign literature using machine translation technology. Leveraging Probability Path analysis and classification techniques, investigate the effectiveness of various algorithms in improving translation accuracy, fluency, and comprehension for English learners. Through the Probability Path analysis, we delve into the complexities of language structures, translation probabilities, learner interaction, and linguistic adaptation, illuminating the intricate dynamics of machine translation in the context of language learning. Additionally, classification results provide insights into the performance of different algorithms in translating diverse literary genres, showcasing their potential to support English learners in comprehending foreign texts. The findings reveal translation accuracies ranging from 87.8% to 92.0%, with corresponding fluency scores ranging from 4.0 to 4.6. Additionally, algorithms result in comprehension improvements of 23% to 30% for English learners.