Toward Tree Substitution Grammars with Latent Annotations

We provide a model that extends the splitmerge framework of Petrov et al. (2006) to jointly learn latent annotations and Tree Substitution Grammars (TSGs). We then conduct a variety of experiments with this model, first inducing grammars on a portion of the Penn Treebank and the Korean Treebank 2.0, and next experimenting with grammar refinement from a single nonterminal and from the Universal Part of Speech tagset. We present qualitative analysis showing promising signs across all experiments that our combined approach successfully provides for greater flexibility in grammar induction within the structured guidance provided by the treebank, leveraging the complementary natures of these two approaches.