Automatic Post-editing of Hierarchical Attention Networks for Improved Context-aware Neural Machine Translation
Publish Year: 1402
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_JADM-11-1_008
تاریخ نمایه سازی: 20 فروردین 1402
Abstract:
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 ۲۲.۹۱ on En-De news-commentary dataset).
Keywords:
Context-Aware Neural Machine Translation , Document-Level Neural Machine Translation , Neural Machine Translation
Authors
M. M. Jaziriyan
Human-Computer Interaction Lab., Faculty of Electrical and Computer Engineering Tarbiat Modares University, Tehran, Iran.
F. Ghaderi
Human-Computer Interaction Lab., Faculty of Electrical and Computer Engineering Tarbiat Modares University, Tehran, Iran.
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