Calibration of Constraint Promotion Does Not Help with Learning Variation in Stochastic Optimality Theory-Reference-Cited by-同舟云学术

Calibration of Constraint Promotion Does Not Help with Learning Variation in Stochastic Optimality Theory

Published:2020-01 Issue:1 Volume:51 Page:97-123
ISSN:0024-3892
Container-title:Linguistic Inquiry
language:en
Short-container-title:Linguistic Inquiry

Author:

Magri Giorgio¹,Storme Benjamin¹

Affiliation:

1. SFL UMR 7023 (UPL, CNRS, University of Paris 8), 61 rue Pouchet, 75017 Paris, France,

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 the CEDRA raises a serious unsolved challenge for learnability research in stochastic Optimality Theory, because the CEDRA itself was proposed to repair a learnability problem ( Pater 2008 ) encountered by the original Gradual Learning Algorithm. This result is supported by both simulation results and a detailed analysis whereby a few constraints and a few candidates at a time are recursively “peeled off” until we are left with a “core” small enough that the behavior of the learner is easy to interpret.

Publisher

MIT Press - Journals

Subject

Linguistics and Language,Language and Linguistics

Link

https://www.mitpressjournals.org/doi/pdf/10.1162/ling_a_00328

Reference16 articles.

1. Deriving Variation from Grammar

2. Anttila, Arto. 1997b. Variation in Finnish phonology and morphology. Doctoral dissertation, Stanford University, Stanford, CA.

3. Boersma, Paul. 1998. Functional phonology. Doctoral dissertation, University of Amsterdam. The Hague: Holland Academic Graphics.

4. Some Correct Error-Driven Versions of the Constraint Demotion Algorithm