AI-Assisted Human Evaluation of Machine Translation

Annually, research teams spend large amounts of money to evaluate the quality of machine translation systems (WMT, inter alia). This is expensive because it requires a lot of expert human labor. The recently adopted annotation protocol, Error Span Annotation (ESA), has annotators marking erroneous parts of the translation and then assigning a final score. A lot of the annotator time is spent on scanning the translation for possible errors. In our work, we help the annotators by pre-filling the error annotations with recall-oriented automatic quality estimation. With this AI assistance, we obtain annotations at the same quality level while cutting down the time per span annotation by half (71s/error span $\rightarrow$ 31s/error span). The biggest advantage of ESA$^\mathrm{AI}$ protocol is an accurate priming of annotators (pre-filled error spans) before they assign the final score. This also alleviates a potential automation bias, which we confirm to be low. In addition, the annotation budget can be reduced by almost 25\% with filtering of examples that the AI deems to be very likely to be correct.