Morphological complexity as a predictor of cognitive effort in neural machine translation post-editing

Abstract This study examines how morphological complexity affects cognitive effort in neural machine translation (NMT) post-editing across six languages. Analysis of the DivEMT dataset shows that morphologically richer target languages like Ukrainian and Turkish require more editing time, keystrokes, and frequent pauses, indicating higher cognitive demands. Vietnamese, despite simpler morphology, also showed high cognitive effort, suggesting other factors like syntax influence processing load. Mean Size of Paradigm (MSP) analysis confirmed Ukrainian and Turkish’s high morphological complexity compared to isolating languages like Vietnamese. Higher error rates in morphologically rich languages demonstrate increased editing needs. While user perceptions varied, the data reveals that greater linguistic distance correlates with higher cognitive effort in NMT post-editing, showing typological divergence impacts beyond morphology alone.