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This paper describes Netmarble's submission to WMT21 Automatic Post-Editing(APE) Shared Task for the English-German language pair. First, we propose aCurriculum Training Strategy in training stages. Facebook Fair's WMT19 newstranslation model was chosen to engage the large and powerful pre-trainedneural networks. Then, we post-train the translation model with differentlevels of data at each training stages. As the training stages go on, we makethe system learn to solve multiple tasks by adding extra information atdifferent training stages gradually. We also show a way to utilize theadditional data in large volume for APE tasks. For further improvement, weapply Multi-Task Learning Strategy with the Dynamic Weight Average during thefine-tuning stage. To fine-tune the APE corpus with limited data, we add somerelated subtasks to learn a unified representation. Finally, for betterperformance, we leverage external translations as augmented machine translation(MT) during the post-training and fine-tuning. As experimental results show,our APE system significantly improves the translations of provided MT resultsby -2.848 and +3.74 on the development dataset in terms of TER and BLEU,respectively. It also demonstrates its effectiveness on the test dataset withhigher quality than the development dataset.

Netmarble AI Center's WMT21 Automatic Post-Editing Shared Task Submission