Functional Typology Approach in Evaluating Human Translation and Machine Translation: A Functional Adequacy Analysis Based on Christiane Nord’s Model

Abstract

The rapid development of neural machine translation has transformed translation practices in academic and professional contexts. However, the ability of machine translation to preserve communicative functions within target texts remains debatable. This study compares the functional quality of Human Translation (HT) and Machine Translation (MT) using Christiane Nord's Functional Typology and functional adequacy framework, emphasizing communicative function, contextual sensitivity, and target-reader orientation as the main indicators of translation quality. A descriptive-comparative design with a mixed-methods approach was employed. Ten English academic texts categorized as informative texts were translated into Indonesian through both human and machine translation. Data were analyzed qualitatively using Nord's extratextual and intratextual factors and quantitatively through a scoring system of five indicators: text-function suitability, semantic accuracy, acceptability, contextual sensitivity, and cohesion-coherence. The findings reveal that Human Translation achieved a mean score of 19.0, categorized as highly adequate, while Machine Translation obtained a mean score of 13.2, categorized as moderately adequate. Human Translation demonstrated superior performance in maintaining academic register and communicative naturalness, whereas Machine Translation tended to produce literal structures with limited contextual adaptation. The novelty of this study lies in operationalizing Nord's functionalist framework into a measurable evaluation model for systematically comparing HT and MT. Functional evaluation proves more comprehensive than purely linguistic metrics in assessing translation quality in the AI-assisted translation era.