Diving Deep into Context-Aware Neural Machine Translation
Jingjing Huo, Christian Herold, Yingbo Gao, Leonard Dahlmann, Shahram Khadivi, Hermann Ney · 2020
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 taskspecific 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.