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.

翻译：大多数现有的文件级神经机翻译模型(NMT)利用现有的全球来源句子固定数量以前或所有句子来处理标准NMT中与背景无关的问题。然而,翻译每种来源句子会因背景大小不同而受益,而且背景不适当,可能会损害翻译性能。在这项工作中,我们引入了一种数据适应方法,使模型能够采用必要和有用的背景。具体地说,我们为两个文件级翻译模型引入了一个灯光预测器,以选择明确的背景。实验表明,拟议的方法可以大大改进以往方法的性能,增加1.99 BLEU点。