Abstract

Equiprobable Mappings in Weighted Constraint Grammars

Authors: Arto Tapani Anttila (Stanford) , Scott Borgeson (Stanford) , Giorgio Magri (CNRS)

Abstract

We show that MaxEnt is so rich that it can distinguish between any two different input-output mappings: there always exists a nonnegative weight vector that assigns them different MaxEnt probabilities. Stochastic HG instead does admit equiprobable mappings, namely mappings that have the same probability for every weight vector, and we give a complete formal characterization of their violation profiles. Empirically, we show that the predictions of MaxEnt and Stochastic HG differ for a grammar of Finnish secondary stress. We test the predictions of both models against a large internet corpus and find preliminary support for Stochastic HG and against MaxEnt.

Keywords: probabilistic phonology, MaxEnt, Stochastic HG, Finnish

How to Cite: Tapani Anttila, A. , Borgeson, S. & Magri, G. (2020) “Equiprobable Mappings in Weighted Constraint Grammars”, Society for Computation in Linguistics. 3(1). doi: https://doi.org/10.7275/yhty-rp63