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This paper presents the Automatic Postediting (APE) systems submitted by the DFKIMLT group to the WMT’18 APE shared task. Three monolingual neural sequenceto-sequence APE systems were trained using target-language data only: one using an attentional recurrent neural network architecture and two using the attention-only (transformer) architecture. The training data was composed of machine translated (MT) output used as source to the APE model aligned with their manually post-edited version or reference translation as target. We made use of the provided training sets only and trained APE models applicable to phrase-based and neural MT outputs. Results show better performances reached by the attention-only model over the recurrent one, significant improvement over the baseline when post-editing phrase-based MT output but degradation when applied to neural MT output.

DFKI-MLT System Description for the WMT18 Automatic Post-editing Task