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.

翻译：与句子级系统相比，文档级神经机器翻译模型能够在整个文档中产生更一致的输出，并更好地解决输入中的歧义问题。目前已有大量关于文档级神经机器翻译的研究，主要集中在修改模型架构或训练策略，以更好地适应额外的上下文输入。然而，在大多数研究中，关于如何利用训练好的模型执行搜索的问题鲜有讨论，有时甚至完全未提及。本研究旨在回答如何最优地利用上下文感知翻译模型进行解码的问题。我们从最常用的文档级神经机器翻译方法入手，比较了不同的解码方案，其中部分来自文献，部分由我们提出。在比较过程中，我们同时采用了标准自动评估指标以及针对三个标准文档级翻译基准测试中的特定语言现象进行评测。研究发现，大多数常用解码策略的表现彼此相似，而更高质量的上下文信息则具有进一步改善翻译效果的潜力。