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.

本文提出了一种新的框架，将用户提供的限制条件作为外部记忆存储，并以柔性方式进行处理，从而解决在现实世界中限制条件可能存在的问题，实验结果表明，我们的方法能够在处理嘈杂的限制条件时取得相当大的 BLEU 收益，这些结果使我们能够将所提出的方法应用于不需要用户帮助生成限制条件的新场景中并证明了该方法确实能够提高自动生成的约束下的翻译质量。