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We describe finite-state constraint relaxation, a method for applying global constraints, expressed as automata, to sequence model decoding. We present algorithms for both hard constraints and binary soft constraints. On the CoNLL-2004 semantic role labeling task, we report a speedup of at least 16x over a previous method that used integer linear programming.
Tromble et al. (Sun,) studied this question.
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