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A major challenge in the practical use of Machine Translation (MT) is thatusers lack guidance to make informed decisions about when to rely on outputs.Progress in quality estimation research provides techniques to automaticallyassess MT quality, but these techniques have primarily been evaluated in vitroby comparison against human judgments outside of a specific context of use.This paper evaluates quality estimation feedback in vivo with a human studysimulating decision-making in high-stakes medical settings. Using EmergencyDepartment discharge instructions, we study how interventions based on qualityestimation versus backtranslation assist physicians in deciding whether to showMT outputs to a patient. We find that quality estimation improves appropriatereliance on MT, but backtranslation helps physicians detect more clinicallyharmful errors that QE alone often misses.

Physician Detection of Clinical Harm in Machine Translation: Quality Estimation Aids in Reliance and Backtranslation Identifies Critical Errors