Structured Analysis of the Transformer Architecture Within the Context of Machine Translation (MT)

Author(s)	DR. S. THILAGAVATHI, DR. P. BALAMUTHUKUMAR
Country	India
Abstract	The introduction of the Transformer architecture in 2017 revolutionized the field of Machine Translation by shifting the paradigm from recurrent, sequential processing to a parallelizable, attention-based framework. By utilizing the self-attention mechanism, the Transformer effectively captures long-range dependencies in text, overcoming the limitations of previous architectures like Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) units. This report analyzes the architecture, its comparative advantages, technical challenges, and its role as the foundation for modern Natural Language Processing (NLP).
Keywords	Machine Translation (MT), Neural Machine Translation, Parallelization, Long-Range Dependency Modeling, Global Context
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 17, Issue 5, May 2026
Published On	2026-05-10
Cite This	Structured Analysis of the Transformer Architecture Within the Context of Machine Translation (MT) - DR. S. THILAGAVATHI, DR. P. BALAMUTHUKUMAR - IJTAS Volume 17, Issue 5, May 2026.

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