It is well-known that document context is vital for resolving a range of translation ambiguities, and in fact the document setting is the most natural setting for nearly all translation. It is therefore unfortunate that machine translation -- both research and production -- largely remains stuck in a decades-old sentence-level translation paradigm. It is also an increasingly glaring problem in light of competitive pressure from large language models, which are natively document-based. Much work in document-context machine translation exists, but for various reasons has been unable to catch hold. This paper suggests a path out of this rut by addressing three impediments at once: what architectures should we use? where do we get document-level information for training them? and how do we know whether they are any good? In contrast to work on specialized architectures, we show that the standard Transformer architecture is sufficient, provided it has enough capacity. Next, we address the training data issue by taking document samples from back-translated data only, where the data is not only more readily available, but is also of higher quality compared to parallel document data, which may contain machine translation output. Finally, we propose generative variants of existing contrastive metrics that are better able to discriminate among document systems. Results in four large-data language pairs (DE$\rightarrow$EN, EN$\rightarrow$DE, EN$\rightarrow$FR, and EN$\rightarrow$RU) establish the success of these three pieces together in improving document-level performance.

翻译：众所周知，文档上下文对于解决多种翻译歧义至关重要，事实上文档场景几乎是所有翻译任务中最自然的设定。因此令人遗憾的是，机器翻译——无论是研究领域还是生产应用——在很大程度上仍停留在数十年前的句子级翻译范式中。面对大型语言模型（其本质是基于文档的）带来的竞争压力，这更成为一个日益凸显的问题。目前虽然已有大量关于文档上下文机器翻译的研究工作，但由于各种原因未能取得实质性突破。本文通过同时解决三个阻碍因素来探寻突破路径：应采用何种架构？如何获取用于训练的文档级信息？以及如何评估模型性能？与专注于专门架构的研究不同，我们证明标准Transformer架构在具备足够容量时即可胜任。其次，我们通过仅从反向翻译数据中提取文档样本来解决训练数据问题：这类数据不仅更易获取，且与可能包含机器翻译输出的平行文档数据相比质量更高。最后，我们提出现有对比度指标的生成式变体，能更好地区分文档级翻译系统。在四个大规模数据语言对（德→英、英→德、英→法、英→俄）上的实验结果证实，这三项改进共同提升了文档级翻译性能。