  Machine translation systems are expected to cope with various types of
constraints in many practical scenarios. While neural machine translation (NMT)
has achieved strong performance in unconstrained cases, it is non-trivial to
impose pre-specified constraints into the translation process of NMT models.
Although many approaches have been proposed to address this issue, most
existing methods can not satisfy the following three desiderata at the same
time: (1) high translation quality, (2) high match accuracy, and (3) low
latency. In this work, we propose a template-based method that can yield
results with high translation quality and match accuracy and the inference
speed of our method is comparable with unconstrained NMT models. Our basic idea
is to rearrange the generation of constrained and unconstrained tokens through
a template. Our method does not require any changes in the model architecture
and the decoding algorithm. Experimental results show that the proposed
template-based approach can outperform several representative baselines in both
lexically and structurally constrained translation tasks.
