Equiprobable mappings in weighted constraint grammars
This work addresses theoretical differences in probabilistic grammar frameworks for linguists, providing incremental insights into their modeling capabilities.
The paper demonstrates that MaxEnt grammars can always distinguish between different mappings with appropriate weights, while Stochastic HG allows equiprobable mappings, which are formally characterized and tested on Finnish stress data.
We show that MaxEnt is so rich that it can distinguish between any two different mappings: there always exists a nonnegative weight vector which assigns them different MaxEnt probabilities. Stochastic HG instead does admit equiprobable mappings and we give a complete formal characterization of them. We compare these different predictions of the two frameworks on a test case of Finnish stress.