Natural Language Processing, Deep Learning, and Text Processing: A Translational Perspective

This study offers a multifaceted examination of Natural Language Processing (NLP) and deep learning models, which are key components of artificial intelligence. In this context, text processing workflows operating on artificial neural networks are discussed with a particular focus on translation. The advancement of artificial neural networks and the widespread adoption of deep learning algorithms have led to significant developments in the field of NLP. Neural machine translation, which has emerged as a transformative development in artificial intelligence, has sparked debates within the translation community. Although it is still under discussion whether neural machine translation can produce translations at a human level of reliability, the speed and convenience it offers undoubtedly contribute to digitalization and automation in the era of Industry 4.0.NLP employs various methods to enable computers to process human language. Among these, deep learning algorithms based on artificial neural networks dominate current applications and neural machine translation systems. Accordingly, diverse text processing workflows are carried out within NLP and deep learning mechanisms. These workflows may vary depending on the architectural characteristics of the language model being used. The study examines recent language model architectures and their properties, with a particular focus on the text processing procedures inherent to these models. These procedures are examined in relation to the language processing mechanisms of the human brain, within the framework of a descriptive analytical approach. The study concludes that NLP and deep learning technologies will play a significant role in the future of the translation profession and emphasizes the necessity for translators to follow technological advancements in response to this transformation.