  Existing document-level neural machine translation (NMT) models have
sufficiently explored different context settings to provide guidance for target
generation. However, little attention is paid to inaugurate more diverse
context for abundant context information. In this paper, we propose a Selective
Memory-augmented Neural Document Translation model to deal with documents
containing large hypothesis space of the context. Specifically, we retrieve
similar bilingual sentence pairs from the training corpus to augment global
context and then extend the two-stream attention model with selective mechanism
to capture local context and diverse global contexts. This unified approach
allows our model to be trained elegantly on three publicly document-level
machine translation datasets and significantly outperforms previous
document-level NMT models.
