Context-Aware Monolingual Repair for Neural Machine Translation
Table 5: Human evaluation results, comparing DocRepair with baseline.
['[BOLD] all', '[BOLD] equal', '[BOLD] better', '[BOLD] worse']
In about 52% of the cases annotators marked translations as having equal quality. Among the cases where one of the translations was marked better than the other, the DocRepair translation was marked better in 73% of the cases. This shows a strong preference of the annotators for corrected translations over the baseline ones.