Exploring the Cognitive Dimensions in Interpreting and AI

Abstract

Anticipation is a fundamental cognitive process in interpreting that enables interpreters to predict upcoming speech segments and facilitate the transfer of meaning between languages. This abstract explores the cognitive aspects of anticipation in interpreting and examines how artificial intelligence (AI) can enhance this process.

Drawing on research from cognitive psychology and interpreting studies, the abstract discusses the cognitive mechanisms involved in anticipation, including the role of working memory, attention, and language processing

 It explores how interpreters utilize anticipation at different levels, such as lexical, syntactic, and semantic anticipation, to produce fluent and coherent interpretations. Furthermore, the abstract examines the potential of AI in supporting interpreters' anticipation skills. It discusses how AI technologies, such as machine learning and natural language processing, can analyze language patterns, predict upcoming speech segments, and provide real-time suggestions to interpreters. The integration of AI in interpreting can augment interpreters' anticipation abilities, improve accuracy, and enhance the overall interpreting experience. However, challenges such as the need for training AI models on diverse language pairs and the importance of maintaining the human interpreter's role and expertise should be considered. Understanding the cognitive aspects of anticipation in interpreting and the potential of AI can inform the development of AI-assisted interpreting tools and advance the field by optimizing the efficiency and quality of interpretation.