Compared to sentence-level systems, document-level neural machine translation
(NMT) models produce a more consistent output across a document and are able to
better resolve ambiguities within the input. There are many works on
document-level NMT, mostly focusing on modifying the model architecture or
training strategy to better accommodate the additional context-input. On the
other hand, in most works, the question on how to perform search with the
trained model is scarcely discussed, sometimes not mentioned at all. In this
work, we aim to answer the question how to best utilize a context-aware
translation model in decoding. We start with the most popular document-level
NMT approach and compare different decoding schemes, some from the literature
and others proposed by us. In the comparison, we are using both, standard
automatic metrics, as well as specific linguistic phenomena on three standard
document-level translation benchmarks. We find that most commonly used decoding
strategies perform similar to each other and that higher quality context
information has the potential to further improve the translation.

本文研究如何利用上下文感知的翻译模型在解码时最佳应用。通过对比使用标准自动指标和三个标准的文档级翻译基准测试上的特定语言现象，我们发现大多数常用的解码策略表现相似，并且更高质量的上下文信息有潜力进一步改善翻译效果。