An Empirical Study on the Effectiveness of AI Tools in Predicting English-Arabic Terminology During Pre-Interpreting Sessions

Abstract

The paper examines the effectiveness of using AI tools in predicting English-Arabic terminology during pre-interpreting sessions. As proper terminology preparation is considered a key component of successful interpretation, AI tools have presented new ways for interpreters to perform their tasks more efficiently and effectively. This paper, therefore, employs an empirical approach in investigating the predictability of AI-selected tools to come up with contextually correct and semantically accurate terms. Controlled experiments involving professional interpreters who evaluate the quality and relevance of the terms generated by the AI are presented. Results show that AI tools help in significant time reduction and increase the accuracy of the terminology while preparing domain-specific terminology. However, several limitations were discovered in contextual discrepancies and domain-specific challenges. This study underlines the potential of AI as a supportive resource in pre-interpreting preparation and further emphasises the need for refinement in AI systems in dealing with contextual and linguistic complexities arising during professional interpreting contexts.

Keywords: AI, Simultaneous Interpreting, Prediction, Terminology , Empirical Study