Easy and Hard Constraint Ranking in OT: Algorithms and Complexity
Jason Eisner
Abstract
We consider the problem of ranking a set of OT constraints in a manner consistent with data. We speed up Tesar and Smolensky's RCD algorithm to be linear on the number of constraints. This finds a ranking so each attested form xi beats or ties a particular competitor yi. We also generalize RCD so each xi beats or ties all possible competitors. Alas, this more realistic version of learning has no polynomial algorithm unless P=NP! Indeed, not even generation does. So one cannot improve qualitatively upon brute force: Merely checking that a single (given) ranking is consistent with given forms is coNP-complete if the surface forms are fully observed and Delta2p-complete if not. Indeed, OT generation is OptP-complete. As for ranking, determining whether any consistent ranking exists is coNP-hard (but in Delta2p) if the forms are fully observed, and Sigma2p-complete if not. Finally, we show that generation and ranking are easier in derivational theories: in P, and NP-complete.
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