Standard context-aware neural machine translation (NMT) typically relies on
parallel document-level data, exploiting both source and target contexts.
Concatenation-based approaches in particular, still a strong baseline for
document-level NMT, prepend source and/or target context sentences to the
sentences to be translated, with model variants that exploit equal amounts of
source and target data on each side achieving state-of-the-art results. In this
work, we investigate whether target data should be further promoted within
standard concatenation-based approaches, as most document-level phenomena rely
on information that is present on the target language side. We evaluate novel
concatenation-based variants where the target context is prepended to the
source language, either in isolation or in combination with the source context.
Experimental results in English-Russian and Basque-Spanish show that including
target context in the source leads to large improvements on target language
phenomena. On source-dependent phenomena, using only target language context in
the source achieves parity with state-of-the-art concatenation approaches, or
slightly underperforms, whereas combining source and target context on the
source side leads to significant gains across the board.

该研究通过拼接语境的方法，研究了在标准文档级神经机器翻译中，是否应进一步提升目标语言的上下文对翻译结果的影响，实验结果表明，在目标语言现象上，将目标语言上下文加入到源语言端能够有显著的提升，而在依赖于源语言的现象方面，将源语言和目标语言的上下文同时加入源语言端能够在所有指标上都有显著的提升。