We present a new approach to encourage neural machine translation to satisfy
lexical constraints. Our method acts at the training step and thereby avoiding
the introduction of any extra computational overhead at inference step. The
proposed method combines three main ingredients. The first one consists in
augmenting the training data to specify the constraints. Intuitively, this
encourages the model to learn a copy behavior when it encounters constraint
terms. Compared to previous work, we use a simplified augmentation strategy
without source factors. The second ingredient is constraint token masking,
which makes it even easier for the model to learn the copy behavior and
generalize better. The third one, is a modification of the standard cross
entropy loss to bias the model towards assigning high probabilities to
constraint words. Empirical results show that our method improves upon related
baselines in terms of both BLEU score and the percentage of generated
constraint terms.

本文提出了一种新的方法，通过训练步骤来鼓励神经机器翻译满足词汇约束，其主要包括训练数据扩充、约束词汇掩码和交叉熵损失函数修改三个方面，实验表明该方法在 BLEU 分数和生成约束词汇的数量方面均优于其他算法。