Transformer Based Neural Machine Translation

Transformer-based neural machine translation (NMT) aims to improve the accuracy and fluency of automated language translation using the transformer architecture, known for its efficient handling of long-range dependencies in text. Current research focuses on optimizing transformer models through hyperparameter tuning, incorporating linguistic knowledge at various levels (e.g., syllable, word, sentence), and addressing challenges like homograph disambiguation and low-resource language translation using techniques such as active learning and data augmentation. These advancements lead to improved translation quality, as measured by metrics like BLEU score, and have applications in diverse fields, including process engineering (e.g., automated diagram generation) and improving the efficiency of NLP tasks.