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The Modal Handling of AI Translation Technologies: A Contrastive Study of Human and Machine Translations of Chinese Instruction Manuals into English
The accuracy of AI translation technologies can be evaluated along one linguistic dimension at a time, and this article focuses on one such dimension, namely modality. Drawing on a corpus of Chinese electronic instruction manuals and their machine and human English translations, this study conducts a contrastive analysis to examine how AI translation systems process modality and evaluates relevant prior findings. The review proceeds in three steps. It first asks whether mainstream systems provide a dedicated procedure for modality and finds that they do not, so output quality still rests on statistical modelling. It then asks what kind of text a manual is and finds that manuals are organised around the directive function, which makes modal expressions appear frequently, especially imperatives. It finally compares the modal systems of the two languages and finds that Chinese and English do not divide modality in the same way, so a given modal meaning has no fixed formal counterpart. The strength of a marker is therefore a matter for the translator to judge in context. Comparisons of human and machine output have registered modal shifts and over-forceful warnings, but most of them remain commentaries on single examples. The only large-scaled quantitative study dates from the statistical MT era and has not been repeated since. A gap therefore remains. Before translating, current systems cannot establish which functional category a sentence belongs to, whether instruction, caution or prohibition, and so cannot choose modal strength from the range that category allows. By linking modal strength to functional categories, this study offers a criterion for future comparisons and a basis for revising machine-translated output before it reaches readers.


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