Transformer-based Automatic Post-Editing with a Context-Aware Encoding Approach for Multi-Source Inputs

Understand

Recent approaches to the Automatic Post-Editing (APE) research have shown that better results are obtained by multi-source models, which jointly encode both source (src) and machine translation output (mt) to produce post-edited sentence (pe).

• Along this trend, we present a new multi-source APE model based on the Transformer.
• To construct effective joint representations, our model internally learns to incorporate src context into mt representation.
• With this approach, we achieve a significant improvement over baseline systems, as well as the state-of-the-art multi-source APE model.