EXPLORING LINGUISTIC COMPETENCE IN ACADEMIC TEXT TRANSLATION BY PROFESSIONALS

Abstract

This study aimed to explore linguistic competence in academic text translation by professionals, focusing on their awareness, dominant linguistic competence and strategies, and the impact of Machine Translation (MT) and AI-driven software on their workflow. The research utilized the PACTE TC’s model (2003), which includes pragmatic, sociolinguistic, textual, grammatical, and lexical sub-competences. A qualitative research design by Creswell Creswell (2018) with a descriptive method was employed to delve deeply into the subjective experiences of professional translators. The surveys were conducted via Google Form from March to April 2024. The results indicate that the respondents demonstrated high awareness of linguistic competence, particularly in pragmatic, sociolinguistic, textual, grammatical, and lexical aspects. Respondents identified linguistic challenges such as lexical, textual, and sociolinguistic issues. Strategies to overcome these challenges included using online resources, human checks, and continuous learning. The results also show varied attitudes towards MT and AI, with some translators embracing these tools for efficiency and others preferred manual methods. MT and AI were perceived to enhance translation quality, especially in grammar accuracy and efficiency. However, the study’s limitations highlight the need for future research on the effectiveness of different MT and AI tools, balancing technological assistance with human expertise, and the impact of training programs.

Keywords

• academic translation, ai-driven software, linguistic competence, machine translation, professional translator