A major challenge in the practical use of Machine Translation (MT) is that
users lack guidance to make informed decisions about when to rely on outputs.
Progress in quality estimation research provides techniques to automatically
assess MT quality, but these techniques have primarily been evaluated in vitro
by comparison against human judgments outside of a specific context of use.
This paper evaluates quality estimation feedback in vivo with a human study
simulating decision-making in high-stakes medical settings. Using Emergency
Department discharge instructions, we study how interventions based on quality
estimation versus backtranslation assist physicians in deciding whether to show
MT outputs to a patient. We find that quality estimation improves appropriate
reliance on MT, but backtranslation helps physicians detect more clinically
harmful errors that QE alone often misses.

机器翻译的实际应用中一个重要的挑战是用户缺乏决策指导，本文通过在高风险的医疗环境中模拟决策过程，评估了质量估计反馈对决策的影响，发现质量估计提高了适当依赖机器翻译的能力，但与仅使用质量估计相比，后翻译帮助医生发现了更多常常被质量估计忽视的临床上有害的错误。