EVALUATING THE EFFECTIVENESS OF MT AND CAT TOOLS IN LITERARY TRANSLATION: A CASE STUDY OF SAADAT HASAN MANTO’S TOBA TEK SINGH

Abstract

This study investigates the impact of Machine Translation (MT) and Computer-Assisted Translation (CAT) tools on the translation quality of Saadat Hasan Manto’s renowned short story Toba Tek Singh. Utilizing Equivalence and Fidelity Theory (Nida & Taber, 1982) as the theoretical framework, the research compares MT/CAT-generated English outputs with a published human translation. The analysis focuses on three core areas: lexical and syntactic accuracy, cultural and contextual equivalence, and emotional and stylistic fidelity. Findings reveal that while MT/CAT tools produce grammatically coherent translations, they frequently fail to capture idiomatic expressions, cultural references, and narrative tone. Key emotional and stylistic elements such as satire, irony, and character voice are often flattened in machine outputs, leading to loss of nuance. In contrast, the human translation preserves the narrative’s complexity, socio-political context, and emotional depth. The study concludes that while MT and CAT tools offer efficiency, they lack the interpretive capacity needed for literary translation. Future work should integrate cultural annotation tools, bilingual feedback, and enhanced AI learning models to bridge current limitations. This research contributes to the growing field of translation studies by highlighting the limitations of automated tools in preserving the integrity of culturally rich texts.