  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.
