Equiprobable Mappings in Weighted Constraint Grammars
- 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), 439-440. doi: https://doi.org/10.7275/yhty-rp63
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