AbstractTasks performed on machine translation (MT) output are associated with input text types such as genre and topic. Predictive Linguistic Assessments of Translation Output, or PLATO, MT Evaluation (MTE) explores a predictive relationship between linguistic metrics and the information processing tasks reliably performable on output. PLATO assigns a linguistic signature, which cuts across the task-based and automated metric paradigms. Here we report on PLATO assessments of clarity, coherence, morphology, syntax, lexical robustness, name-rendering, and terminology in a comparison of Arabic MT engines in which register differentiates the input. With a team of 10 assessors employing eight linguistic tests, we analyzed the results of five systems processing of 10 input texts from two distinct linguistic registers: a total we analyzed 800 data sets. The analysis pointed to specific areas, such as general lexical robustness, where system performance was comparable on both types of input. Divergent performance, however, was observed on clarity and name-rendering assessments. These results suggest that, while systems may be considered reliable regardless of input register for the lexicon-dependent triage task, register may have an affect on the suitability of MT systems output for relevance judgment and information extraction tasks, which rely on clearness and proper named-entity rendering. Further, we show that the evaluation metrics incorporated in PLATO differentiate between MT systems performance on a text type for which they are presumably optimized and one on which they are not.