  Existing work in document-level neural machine translation commonly
concatenates several consecutive sentences as a pseudo-document, and then
learns inter-sentential dependencies. This strategy limits the model's ability
to leverage information from distant context. We overcome this limitation with
a novel Document Flattening (DocFlat) technique that integrates Flat-Batch
Attention (FBA) and Neural Context Gate (NCG) into Transformer model to utilize
information beyond the pseudo-document boundaries. FBA allows the model to
attend to all the positions in the batch and learns the relationships between
positions explicitly and NCG identifies the useful information from the distant
context. We conduct comprehensive experiments and analyses on three benchmark
datasets for English-German translation, and validate the effectiveness of two
variants of DocFlat. Empirical results show that our approach outperforms
strong baselines with statistical significance on BLEU, COMET and accuracy on
the contrastive test set. The analyses highlight that DocFlat is highly
effective in capturing the long-range information.
