Machine Translation vs. Human Translation: A Comparative Study of Translation Quality

Abstract

This research is deeply rooted into the ongoing debate surrounding machine translation (MT) and human translation (HT) by conducting a comprehensive comparative study of translation quality between the two methodologies. With the rapid advancements in artificial intelligence and natural language processing, machine translation systems have made significant progress towards the solution of real-life problems. By raising questions about their efficacy compared to the nuanced understanding and linguistic finesse inherent in human translation. Through a meticulous examination of various linguistic aspects such as accuracy, fluency, cultural sensitivity, and context comprehension, this study aims to provide empirical evidence on the strengths and limitations of both MT and HT. By analyzing a diverse set of text samples across multiple languages and domains, including literature, technical documents, and colloquial speech, we endeavor to offer insights into the relative performance of MT and HT in different translation scenarios. Furthermore, this research seeks to explore the evolving roles of MT and HT in the contemporary translation landscape and their potential implications for language professionals, technology developers, and end-users. Ultimately, this comparative study endeavors to contribute to a deeper understanding of the complex dynamics between machine and human translation, shedding light on the optimal utilization of both approaches to achieve superior translation outcomes.