International Journal of Technology and Applied Science

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Automated Multilingual Translation Using Neural Machine Translation and Transformer Architecture

Author(s) Mr. Chinmaya MD, Dr. Supreetha Gowda HD
Country India
Abstract Language barriers continue to limit communication, education, and access to information across global digital platforms. Conventional rule-based and statistical machine translation systems frequently fail to capture sentence-level context, grammar, and semantic meaning, producing inaccurate or unnatural translations, particularly for idiomatic expressions and morphologically complex languages. This paper presents an automated multilingual translation system built around a Transformer-based Neural Machine Translation (NMT) model that leverages multi-head self-attention to translate text between English, Hindi, French, Spanish, and German with contextual awareness. The system integrates automatic source-language detection, a text preprocessing pipeline (cleaning, normalization, subword tokenization), attention-based translation explainability, and a web-based interface supporting real-time translation, translation-history storage, and downloadable reports. The Transformer model is fine-tuned on multilingual parallel corpora drawn from the OPUS and WMT repositories using an 80:10:10 train/validation/test split. Evaluation on a held-out multilingual test set using BLEU, ROUGE-L, and translation-accuracy metrics shows a macro-average translation accuracy of 92.7%, exceeding a 90% target, with a mean inference time of 178.5 ms per request. Comparative evaluation against LSTM-based, GRU-based, statistical, and rule-based baselines shows the proposed Transformer model outperforming all four alternatives on both accuracy and BLEU score. These results indicate that combining attention-based Transformer translation with practical deployment features — language detection, explainability, history management, and reporting — can deliver an accurate, scalable, and user-accessible multilingual translation platform.
Keywords Neural Machine Translation; Transformer architecture; self-attention; multilingual translation; language detection; BLEU score.
Field Computer Applications
Published In Volume 17, Issue 7, July 2026
Published On 2026-07-07
DOI https://doi.org/10.71097/IJTAS.v17.i7.1359
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Crossref DOI prefix of IJTAS is 10.71097/IJTAS

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