Towards a Robuster Interpretive Parsing
T. Biró · Journal of Logic Language and Information · 2013
Abstract The input data to grammar learning algorithms often consist ofovert formsthat do not contain full structural descriptions. This lack of information may contribute to the failure of learning. Past work onOptimality TheoryintroducedRobust Interpretive Parsing(RIP) as a partial solution to this problem. We generalize RIP and suggest replacing the winner candidate with a weighted mean violation of the potential winner candidates. A Boltzmann distribution is introduced on the winner set, and the distribution’s parameter $$T$$ is gradually decreased. Finally, we show that GRIP, theGeneralized Robust Interpretive Parsing Algorithmsignificantly improves the learning success rate in a model with standard constraints for metrical stress assignment.