This paper proposes a simple and effective algorithm for incorporating
lexical constraints in neural machine translation. Previous work either
required re-training existing models with the lexical constraints or
incorporating them during beam search decoding with significantly higher
computational overheads. Leveraging the flexibility and speed of a recently
proposed Levenshtein Transformer model (Gu et al., 2019), our method injects
terminology constraints at inference time without any impact on decoding speed.
Our method does not require any modification to the training procedure and can
be easily applied at runtime with custom dictionaries. Experiments on
English-German WMT datasets show that our approach improves an unconstrained
baseline and previous approaches.

本文提出了一种在神经机器翻译中加入词汇约束的简单有效算法，该算法可以在推理时注入术语约束，而不影响解码速度，并且无需修改训练流程，使用自定义字典即可运行，实验结果表明，我们的方法在英德 WMT 数据集上可以提高基线和之前方法的翻译质量。