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This study investigates the role of schema theory in translation and aims to gain knowledge that may progress AI-related domains through the simulation of cognition and AI. Schema theory hypothesizes that translators use mental frameworks (schemata) to organize and interpret information. In the context of translation, schemata play a crucial role in knowledge representation, affecting limitations made during the translation process. A cognitive approach is employed and qualitative methods are used in the analysis. A corpus is collected from the translation of a SL Arabic novel (Frankenstein in Baghdad by A. Saadawi) into TL English, covering domains and language pairs. The translation process is analyzed to identify cognitive patterns employed by the translator as they apply schemata to achieve cross-cultural renditions. Conclusions reveal that schema activation significantly impacts translation choices, influencing how the translator interprets and conveys meaning across languages. Cognitive patterns shed light on how cultural and linguistic factors influence the transfer of information between SL and TL. Additionally, the study uncovers variations in schema utilization across different translation tasks and the adaptability and flexibility of cognitive processes in response to varying contexts and linguistic challenges. By deepening our understanding of schema theory, this research contributes to the design, training, and assessment of AI algorithms. It also provides valuable insights into the cognitive mechanisms underlying successful cross-linguistic communication and offers practical implications for translators engaged in intercultural exchange. Finally, this paper recommends that AI systems can drive advancements in MT and other AI-related fields of prior knowledge to produce accuracy and more closely with native speakers by using schema theory concepts.
Mohammed Nihad Ahmed (Sun,) studied this question.