A naive approach to decoding with constraints would be to use a large beam size and select from the set of complete hypotheses the best that satis-fies all constraints.
this is infeasible in practice because it would require searching a potentially very large space to ensure that even hypotheses with low model score due to the inclusion of a constraint would be part of the set of outputs.