We overview vowel harmony computationally, describing necessary and sufficient conditions on phonotactics, processes, and learning.
We overview the notion of phonological abstractness, various types of evidence for it, and consequences for linguistics and psychology.
We derive the well-studied subregular classes of formal languages, which computationally characterize natural language typology, purely from the perspective of algorithmic learning problems.
This article examines whether the computational properties of phonology hold across spoken and signed languages, using model theory and logical transductions.
We analyze the expressivity of a variety of recurrent encoder-decoder networks, showing they are limited to learning subsequential functions, and connecting RNNs with attention mechanisms to a class of deterministic 2-way transducers.
We provide an automata-theoretic characterization of templatic morphology, extending strict locality to consider n-ary functions.
We provide an automata-theoretic characterization of tonal phonology, extending strict locality to consider n-ary functions.
This chapter examines the brief but vibrant history of learnability in phonology.
I provide a vector space characterization of the Star-Free and Locally Threshold testable classes of formal languages, over arbitrary data structures.
We describe a partial order on the space of model-theoretic constraints and a learning algorithm for constraint inference.