Proceedings of the Annual Meeting of the Cognitive Science Society

Acquiring phonological rules is hard, especially when they do not describe generalizations that hold for all surface forms. W e believe it can be made easier by adopting a more cognitively natural architecture for phonological processing. W e briefly review the structure of M^P, our connectionist Many Maps Model of Phonology, in which extrinsic rule ordering is virtually eliminated, and "iterative" processes arc handled by a parallel clustering mechanism. W e then describe a program for inducing phonological rules from examples. Our examples, drawn from Yawelmani, involve several complex rule interactions. The parallel nature of M ^ P rule application greatly simplifies the description of these phenomena, and makes a computational model of rule acquisition feasible.