• Harikrishnan

Machine Translation is one of the NLP task, model that translates the given input text source language to another target language using Deep Learning models and it follows the sequence of process steps text preprocessing, Linguistic Analysis, word Embeddings, Language Modelling and Translate text Generation. The advancement of Language Translation has been significantly driven by Artificial Intelligent Deep Learning language models. In this paper aimed to translate the text from English to German Language and do the enhancement of existing application and evaluate the performance, Language Translation developed by using Deep Learning neural network models, transformers based models and Autoregressive based models. This paper explores the design and working functionality of a Language Translation based on intelligent pretrained NLP models and highlighting the key components used in models , methodologies employed in modern systems. These systems utilize NLP pre-trained models like LSTM and seq-seq models encoder and decoder, attention mechanism, Transformer Models which are fine-tuned for specific language pairs to achieve high-quality translations. The paper discusses the role of NLP and techniques, including neural machine translation (NMT), and encoder-decoder architectures, attention mechanisms, Transformer Architecture (generates human like output results) by enhancing the accuracy, fluency, and contextual understanding of translations. Furthermore, we address the challenges associated with Language translation, low-resource languages, and the computational demands of these models. Finally, the paper explores enhancement of existing system and developed multilanguage modal translation systems using LLMs based Google Translator(internally using NMT and attention mechanism, multi head Transformer architecture), as well as the potential for zero-shot examples. and analyze more about performance through an examinate , Evaluate the base model and Multi Language Model Language Translation performance by using BLEU score. this paper provides insights into the capabilities and future of Language Translation systems based on intelligent language models.

Language Translation, Machine Translation MT, NMT Neural Machine Translation, seq-seq encoder decoder, Attention Mechanism, Transformer Architecture. LLM, Multi Language Translator. BLEU.

" Language Translation using Efficient pretrained NLP Models ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.c501-c509, March-2025, Available :http://www.jetir.org/papers/JETIR2503262.pdf

" Language Translation using Efficient pretrained NLP Models ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppc501-c509, March-2025, Available at : http://www.jetir.org/papers/JETIR2503262.pdf