Physician Detection of Clinical Harm in Machine Translation: Quality
  Estimation Aids in Reliance and Backtranslation Identifies Critical Errors
Nikita Mehandru Sweta Agrawal Yimin Xiao Elaine C Khoong Ge Gao Marine Carpuat Niloufar Salehi
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.