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.

本文研究了文档级 NMT 模型在四个不同领域中的性能，并发现不同的架构适用于不同的任务，同时发现上下文感知系统在任务特定问题上有改进，而文本级别的指标（如 BLEU）没有显着改进。同时，本文还表明，文档级别的回译对于缺乏文档级别双文本的情况有很大的帮助。