Users of machine translation (MT) may want to ensure the use of specific
lexical terminologies. While there exist techniques for incorporating
terminology constraints during inference for MT, current APE approaches cannot
ensure that they will appear in the final translation. In this paper, we
present both autoregressive and non-autoregressive models for lexically
constrained APE, demonstrating that our approach enables preservation of 95% of
the terminologies and also improves translation quality on English-German
benchmarks. Even when applied to lexically constrained MT output, our approach
is able to improve preservation of the terminologies. However, we show that our
models do not learn to copy constraints systematically and suggest a simple
data augmentation technique that leads to improved performance and robustness.

本文提出了自回归模型和非自回归模型用于词汇约束的自适应编辑，实验表明可保留 95% 的术语并提高英德基准翻译质量；虽然应用于词汇约束 MT 输出，但模型学习不会系统地复制约束，因此提出并使用简单的数据增强技术以提高性能和鲁棒性。