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In Machine Translation, assessing the quality of a large amount of automatictranslations can be challenging. Automatic metrics are not reliable when itcomes to high performing systems. In addition, resorting to human evaluatorscan be expensive, especially when evaluating multiple systems. To overcome thelatter challenge, we propose a novel application of online learning that, givenan ensemble of Machine Translation systems, dynamically converges to the bestsystems, by taking advantage of the human feedback available. Our experimentson WMT'19 datasets show that our online approach quickly converges to the top-3ranked systems for the language pairs considered, despite the lack of humanfeedback for many translations.

Online Learning Meets Machine Translation Evaluation: Finding the Best Systems with the Least Human Effort