Log-linear Combinations of Monolingual and BilingualNeural Machine Translation Models for Automatic Post-Editing
Table 3: Results on unseen test set in comparison to other shared task submissions as reported by the task organizers. For submissions by other teams we include only their best result.
['System', 'TER', 'BLEU']
We submitted the output of the last system (mt→pe×4 / src→pe×4 / pep) as our final proposition for the APE shared task, and mt→pe×4 as a contrastive system. Results are sorted by TER from best to worse. For our best system, we see improvements of -3.2% TER and +5.5% BLEU over the unprocessed baseline 1 (uncorrected MT), and -1.5% TER and +1.5% BLEU over our contrastive system. The organizers also provide results for a standard phrase-based Moses set-up (baseline 2) that can hardly beat baseline 1 (-0.1% TER, +1.4% BLEU). Both our systems outperform the next-best submission by large margins. In the light of these last results, our system seems to be quite successful.