Automated Multilingual Translator Using Neural Translation
TL;DR
The results indicate that a moderately sized, shared self-attention architecture can deliver production-quality multilin-gual translation within the resource constraints of an academic de-ployment, while surfacing clear directions – low-resource language coverage, domain adaptation, and speech-based extension – for con-tinued development.
Abstract
Cross-lingual communication remains constrained by translation tools that fail to preserve sentence-level meaning, par-ticularly for idiomatic phrasing, long or multi-clause sentences, and low-resource languages. This paper presents the design and evaluation of a browser-accessible multilingual translation plat-form built around a Transformer encoder–decoder network. A sin-gle shared model is fine-tuned across five languages – English, Hindi, French, Spanish, and German – using subword vocabu-lary construction, automatic source-language identification, and an attention-based explainability layer that exposes token-level align-ment through heatmap visualization. The system was evaluated on a held-out multilingual test set using BLEU, ROUGE-L, and accuracy metrics, achieving a macro-averaged translation accuracy of 92.7%, exceeding a 90% target and outperforming LSTM, GRU, statistical, and rule-based baselines by a wide margin. Average inference la-tency remained below 185 milliseconds per request, supporting in-teractive use. The results indicate that a moderately sized, shared self-attention architecture can deliver production-quality multilin-gual translation within the resource constraints of an academic de-ployment, while surfacing clear directions – low-resource language coverage, domain adaptation, and speech-based extension – for con-tinued development