Study on Machine Translation Teaching Model Based on Translation Parallel Corpus and Exploitation for Multimedia Asian Information Processing

Abstract

Text in one language can be mechanically translated into another language using machine translation (MT). It is possible to anticipate a sequence of words, generally modeling full sentences using machine translation in a single integrated model. Human language's flexibility makes automatic translation an artificial intelligence (AI) challenge of the highest order. A single model rather than a pipeline of fine-tuned models is now the best way to attain state-of-the-art outcomes in machine translation. For example, words having numerous meanings, phrases that use more than one grammatical structure, and other grammar issues make it difficult for a machine to translate; however, many misinterpretations translate to be a breeze. A teacher's job is to assist pupils in overcoming the emotional and cognitive obstacles that stand in the way of developing effective problem-solving abilities. Students will benefit from developing problem-solving abilities since they will apply what they have learned to new circumstances. MT-AI, machine translation technology, and products have been employed in a wide range of applications, including business travel, tourism, and cross-lingual information retrieval. Text translation and phonetic translation are two types of translations that focus on the content of the source language. It is possible to create self-learning systems by injecting machine learning techniques into existing software and then observing the results of such injection. Computer software can translate a massive volume of text in a short period. It takes longer for a human translator to perform the same work as a computer program. The simulation investigation is developed based on correctness and effectiveness, demonstrating the proposed framework's reliability of 95.1%.