Magri and Storme: to appear. Calibration of constraint promotion does not help with learning variation in stochastic OT.

Direct link: http://roa.rutgers.edu/content/article/files/1769_giorgio_magri_1.pdf

ROA: 1349
Title: Calibration of constraint promotion does not help with learning variation in stochastic OT
Authors: Giorgio Magri, Benjamin Storme
Comment: to appear in Linguistic Inquiry
Length: 24 pages + 11 pages of supplementary material
Abstract: The Calibrated error-driven ranking algorithm (CEDRA; Magri 2012) is shown to fail on two test cases of phonologically conditioned variation from Boersma and Hayes (2001). The failure of CEDRA raises a serious unsolved challenge for learnability research in stochastic OT, because CEDRA itself was proposed to repair a learnability problem (Pater 2008) encountered by the original GLA. This result is supported by both simulation results and a detailed analysis whereby a few constraints and a few candidates at the time are recursively ‘peeled off’ until we are left with a ‘core’ small enough that the behavior of the learner is easy to interpret.
Type: Paper/tech report
Area/Keywords: Stochastic OT; learnability analysis
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