Context-Aware Monolingual Repair for Neural Machine Translation
Table 2: BLEU scores. For CADec, the original implementation was used.
['[BOLD] model', '[BOLD] BLEU']
(we evaluate translations of 4-sentence fragments). To see which part of the improvement is due to fixing agreement between sentences rather than simply sentence-level post-editing, we train the same repair model at the sentence level. Each sentence in a group is now corrected separately, then they are put back together in a group. One can see that most of the improvement comes from accounting for extra-sentential dependencies. DocRepair outperforms the baseline and CADec by 0.7 BLEU, and its sentence-level repair version by 0.5 BLEU.