%0 Journal Article %T Automatic Post-editing of Hierarchical Attention Networks for Improved Context-aware Neural Machine Translation %J Journal of AI and Data Mining %I Shahrood University of Technology %Z 2322-5211 %A Jaziriyan, M. M. %A Ghaderi, F. %D 2023 %\ 01/01/2023 %V 11 %N 1 %P 95-102 %! Automatic Post-editing of Hierarchical Attention Networks for Improved Context-aware Neural Machine Translation %K Context-Aware Neural Machine Translation %K Document-Level Neural Machine Translation %K Neural Machine Translation %R 10.22044/jadm.2022.12152.2367 %X 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). %U https://jad.shahroodut.ac.ir/article_2696_3460b42413c63f857cb7e95f40982fc1.pdf