  Most existing document-level neural machine translation (NMT) models leverage
a fixed number of the previous or all global source sentences to handle the
context-independent problem in standard NMT. However, the translating of each
source sentence benefits from various sizes of context, and inappropriate
context may harm the translation performance. In this work, we introduce a
data-adaptive method that enables the model to adopt the necessary and useful
context. Specifically, we introduce a light predictor into two document-level
translation models to select the explicit context. Experiments demonstrate the
proposed approach can significantly improve the performance over the previous
methods with a gain up to 1.99 BLEU points.
