A Bayesian Model for Generative Transition-based Dependency Parsing
Table 3: Effect of including elements in the model conditioning contexts. Results are given on the YM development set.
['Context elements', 'UAS', 'LAS']
The first modelling choice is the selection and ordering of elements in the conditioning contexts of the HPYP priors. The first two words on the stack are the most important, but insufficient – second-order dependencies and further elements on the stack should also be included in the contexts. The challenge is that the back-off structure of each HPYP specifies an ordering of the elements based on their importance in the prediction. We are therefore much more restricted than classifiers with large, sparse feature-sets which are commonly used in transition-based parsers. Due to sparsity, the word types are the first elements to be dropped in the back-off structure, and elements such as third-order dependencies, which have been shown to improve parsing performance, cannot be included successfully in our model.