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.

翻译：标准上下文感知神经机器翻译（NMT）通常依赖并行文档级数据，利用源语言和目标语言的上下文。基于拼接的方法（尤其是文档级NMT的强基线方法）将源语言和/或目标语言的上下文句子拼接到待翻译句子之前，其中利用两侧等量源语言和目标数据的模型变体取得了最先进的结果。本研究探讨在标准拼接方法中是否应进一步促进目标数据的使用，因为大多数文档级现象依赖于目标语言侧的信息。我们评估了新的拼接变体，将目标语言上下文拼接到源语言之前，可单独使用或与源语言上下文结合。英俄和巴斯克-西班牙语的实验结果表明，在源语言中包含目标语言上下文可显著改善目标语言现象。在依赖源语言的现象上，仅使用源语言中的目标语言上下文与最先进的拼接方法效果相当或略逊一筹，而在源语言侧结合源语言和目标语言上下文则在所有指标上带来显著提升。