Abstract:
The term machine translation (MT) refers to automated systems that generate translations, either with or without human intervention. Since the 1950s, there has been a significant evolution in this field, primarily driven by the need for quick and accurate translation of various types of materials. From Rules-Based Machine Translation (RBMT) to the Statistical Machine Translation (SMT), there are currently two systems that cover the domain of machine translation, DeepL Translate, which represents the domain of Neural Machine Translation (NMT), and ChatGPT, which represents the domain of Large Language Model (LLM). Although they both use artificial intelligence, their end products differ in various ways. After outlining the history of machine translation in the first chapter, this dissertation focuses on comparing ChatGPT and DeepL Translate by testing their translation of an abstract and a full paragraph from the article Re-thinking Tourist Wellbeing: An Integrative Model of Affiliation with Nature and Social Connections. This comparison is discussed in Chapter Two. Finally, Chapter Three explores the effectiveness of a completely human translation performed without any assistance from the Internet.
