Giorgio Magri‎ > ‎

Calibration of the promotion amount does not help with learning variation in stochastic Optimality Theory

Downloads:
the paper is available here; the list of tables is available here; the appendix is available here.


Abstract: 
The Calibrated Error-Driven Ranking Algorithm (CEDRA; see here) 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.

Bibtex entry:
@ARTICLE{MagriStorme(2019), AUTHOR = {Magri, Giorgio and Storme, Benjamin}, TITLE = {Calibration of the promotion amount does not help with learning variation in stochastic Optimality Theory}, JOURNAL = {Linguistic Inquiry}, VOLUME = {}, PAGES = {}, YEAR = {to appear} }