Learning Underlying Representations and Input-Strictly-Local Functions

Abstract

The simultaneous inference of underlying representations (URs) and a phonological grammar from alternating surface representations (SRs) in a morphological paradigm is a core problem in phonological learning that only recently has seen progress (Tesar, 2014; Cotterell et al., 2015; Rasin et al., 2018). This paper proposes a learning algorithm that infers URs and phonological processes from SRs based on the hypothesis that phonological generalizations belong to restrictive subregular regions in the Chomsky Hierarchy (Heinz, 2018). We give a procedure that, given sequences of morphemes paired with SRs, learns URs and a phonological grammar that is an input strictly local (ISL; Chandlee, 2014; Chandlee & Heinz, 2018) function. ISL functions are exactly those which make changes in the output with respect to the local information in the input. For now, the procedure is restricted to simplex ISL processes; that is, those exhibiting a single change. However, this illustrates that restrictive computational principles, combined with major principles in phonological analysis, allow for significant progress in understanding how phonological grammars and URs are learned. The paper is organized as follows. Section 2 briefly introduces the paradigm of the learning algorithm. Section 3 discusses the computational structure encoded in the learner. Section 4 is a detailed explanation of the algorithm with a simple example as illustration. Section 5 compares this algorithm with other algorithms and presents its advantages. The last section concludes the paper.