Connor Mayer


Capturing gradience in long-distance phonology using probabilistic tier-based strictly local grammars
Connor Mayer
Proceedings of the Society for Computation in Linguistics 2021


Phonotactic learning with neural language models
Connor Mayer | Max Nelson
Proceedings of the Society for Computation in Linguistics 2020


Sanskrit n-Retroflexion is Input-Output Tier-Based Strictly Local
Thomas Graf | Connor Mayer
Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology

Sanskrit /n/-retroflexion is one of the most complex segmental processes in phonology. While it is still star-free, it does not fit in any of the subregular classes that are commonly entertained in the literature. We show that when construed as a phonotactic dependency, the process fits into a class we call input-output tier-based strictly local (IO-TSL), a natural extension of the familiar class TSL. IO-TSL increases the power of TSL’s tier projection function by making it an input-output strictly local transduction. Assuming that /n/-retroflexion represents the upper bound on the complexity of segmental phonology, this shows that all of segmental phonology can be captured by combining the intuitive notion of tiers with the independently motivated machinery of strictly local mappings.