Diving Deep into Context-Aware Neural Machine Translation

• URL: http://arxiv.org/abs/2010.09482v1
• Date: Mon, 19 Oct 2020 13:23:12 GMT
• Title: Diving Deep into Context-Aware Neural Machine Translation
• Authors: Jingjing Huo, Christian Herold, Yingbo Gao, Leonard Dahlmann, Shahram Khadivi, and Hermann Ney
• Abstract summary: This paper analyzes the performance of document-level NMT models on four diverse domains. We find that there is no single best approach to document-level NMT, but rather that different architectures come out on top on different tasks.
• Score: 36.17847243492193
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Context-aware neural machine translation (NMT) is a promising direction to improve the translation quality by making use of the additional context, e.g., document-level translation, or having meta-information. Although there exist various architectures and analyses, the effectiveness of different context-aware NMT models is not well explored yet. This paper analyzes the performance of document-level NMT models on four diverse domains with a varied amount of parallel document-level bilingual data. We conduct a comprehensive set of experiments to investigate the impact of document-level NMT. We find that there is no single best approach to document-level NMT, but rather that different architectures come out on top on different tasks. Looking at task-specific problems, such as pronoun resolution or headline translation, we find improvements in the context-aware systems, even in cases where the corpus-level metrics like BLEU show no significant improvement. We also show that document-level back-translation significantly helps to compensate for the lack of document-level bi-texts.

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