  Context-aware neural machine translation aims to use the document-level
context to improve translation quality. However, not all words in the context
are helpful. The irrelevant or trivial words may bring some noise and distract
the model from learning the relationship between the current sentence and the
auxiliary context. To mitigate this problem, we propose a novel end-to-end
encoder-decoder model with a layer-wise selection mechanism to sift and refine
the long document context. To verify the effectiveness of our method, extensive
experiments and extra quantitative analysis are conducted on four
document-level machine translation benchmarks. The experimental results
demonstrate that our model significantly outperforms previous models on all
datasets via the soft selection mechanism.
