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BAS-ALSTM: analyzing the efficiency of artificial intelligence-based English translation system

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Abstract

The progression of education reform causes the maximization of the English language’s importance in the learning process. The students require immediate English learning concerns to improve their understanding and learning in education. During the study, Machine Translation (MT) helps to simplify the learning process and helps to understand the huge volume of legal documents with minimum error in legal translations. The MT process utilizes Artificial Intelligence (AI) techniques while translating the text by incorporating the Google API translation techniques. However, the existing systems fail to correlate the user reading experience with system performance. The AI-MT systems are trained with the help of a set of words and text that are having difficulties while giving a large input volume. Therefore, the system faces computation complexity and decreases translation accuracy. The research issue is overcome by incorporating edge computing solutions with user data. The user inputs are investigated using the skip-gram model that generates the input feature vector. Then Beetle Antennae Search with Attention Long Short-Term Memory (BAS-ALSTM) neural model is applied to develop the ranking value for text. The generated word rank helps identify the edge server, and the respective translation process is initiated to maximize the translation matching degree. This process is utilized in the English learning process to increase language understanding ability and reduce learning difficulties. The effective utilization of the neural model understands every text, and translation is performed with the user query. The created system efficiency is evaluated using the experimental results and discussions.

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  1. Liuzhou Railway Vocational Technical College, Liuzhou, 545616, Guangxi, People’s Republic of China

    Si Li & Ying Huang

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  1. Si Li
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  2. Ying Huang
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Correspondence to Ying Huang.

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Li, S., Huang, Y. BAS-ALSTM: analyzing the efficiency of artificial intelligence-based English translation system. J Ambient Intell Human Comput 15, 765–777 (2024). https://doi.org/10.1007/s12652-023-04735-1

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