Automatic Post-editing of Hierarchical Attention Networks for Improved Context-aware Neural Machine Translation | |
| Journal of AI and Data Mining | |
| دوره 11، شماره 1، فروردین 2023، صفحه 95-102 اصل مقاله (1.29 M) | |
| نوع مقاله: Original/Review Paper | |
| شناسه دیجیتال (DOI): 10.22044/jadm.2022.12152.2367 | |
| نویسندگان | |
| M. M. Jaziriyan؛ F. Ghaderi* | |
| Human-Computer Interaction Lab., Faculty of Electrical and Computer Engineering Tarbiat Modares University, Tehran, Iran. | |
| چکیده | |
| 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). | |
| کلیدواژهها | |
| Context-Aware Neural Machine Translation؛ Document-Level Neural Machine Translation؛ Neural Machine Translation | |
| مراجع | |
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