Phonological learning with output-driven maps

ABSTRACT

The concept of an output-driven map formally characterizes an intuitive notion about phonology: that disparities between the input and the output are introduced only to the extent necessary to satisfy restrictions on outputs. When all of the grammars definable in a phonological system are output-driven, the implied structure provides significant computational benefits to language learners. An output-driven map imposes significant structure on the space of possible inputs for words, which can allow a learner to efficiently learn a lexicon of phonological underlying forms despite the vast number of possible lexica, as well as contend with the challenges of map/lexicon interactions inherent in phonological learning. This article presents a learning algorithm that exploits the structure of output-driven maps, illustrated with a system of grammars based in Optimality Theory. The algorithm highlights the roles played by contrast and paradigmatic information in phonological learning.

Acknowledgments

I would like to thank the organizers and participants of GALANA 6, at the University of Maryland in 2015, for helpful comments and feedback. Over the past several years, this work has benefited from conversations with Crystal Akers, Eric Baković, Karen Campbell, Paul de Lacy, Jane Grimshaw, Fernando Guzmán, Brett Hyde, Gara Jarosz, John McCarthy, Nazarré Merchant, Alan Prince, Jason Riggle, and Paul Smolensky. Some of the figures and examples in this article are reprinted with the permission of Cambridge University Press, having previously appeared in Tesar (2014) and are © Bruce Tesar 2014.

Notes

¹ For further discussion of an algorithm for selecting contrast pairs, see Tesar (2014:334–336).