Neural Machine Translation - Architectures and Evaluation: Analyzing neural machine translation (NMT) architectures and evaluation metrics for translating text between different languages

Abstract

Neural Machine Translation (NMT) has revolutionized the field of machine translation, offering significant improvements over traditional statistical approaches. This paper provides a comprehensive analysis of NMT architectures and evaluation metrics. We discuss various NMT architectures, including sequence-to-sequence models, attention mechanisms, and transformer networks, highlighting their strengths and weaknesses. Additionally, we review evaluation metrics such as BLEU, TER, and METEOR, assessing their effectiveness in measuring translation quality. Through this analysis, we aim to provide insights into the current state of NMT research and identify future directions for improving translation quality and efficiency.