  Lexically constrained decoding for machine translation has shown to be
beneficial in previous studies. Unfortunately, constraints provided by users
may contain mistakes in real-world situations. It is still an open question
that how to manipulate these noisy constraints in such practical scenarios. We
present a novel framework that treats constraints as external memories. In this
soft manner, a mistaken constraint can be corrected. Experiments demonstrate
that our approach can achieve substantial BLEU gains in handling noisy
constraints. These results motivate us to apply the proposed approach on a new
scenario where constraints are generated without the help of users. Experiments
show that our approach can indeed improve the translation quality with the
automatically generated constraints.
