automatic post-editing of hierarchical attention networks for improved context-aware neural machine translation

Most of the existing neural machine translation (nmt) methods translate sentences without considering the context. it is shown that exploiting inter and intra- sentential context can improve the nmt models and yield to better overall translation quality. however, providing document-level data is costly, so properly exploiting contextual data from monolingual corpora would help translation quality. in this paper, we proposed a new method for context-aware neural machine translation (ca-nmt) using a combination of hierarchical attention networks (han) and automatic post-editing (ape) techniques to fix discourse phenomena when there is lack of context. han is used when we have a few document-level data, and ape can be trained on vast monolingual document- level data to improve results further. experimental results show that combining han and ape can complement each other to mitigate contextual translation errors and further improve ca-nmt by achieving reasonable improvement over han (i.e., bleu score of 22.91 on en-de news-commentary dataset).