Fast and Robust Multilingual Dependency Parsing with a Generative Latent Variable Model
Ivan S. Titov, James Henderson · 2007
We use a generative history-based model to predict the most likely derivation of a de-pendency parse. Our probabilistic model is based on Incremental Sigmoid Belief Net-works, a recently proposed class of la-tent variable models for structure predic-tion. Their ability to automatically in-duce features results in multilingual pars-ing which is robust enough to achieve accu-racy well above the average for each indi-vidual language in the multilingual track of the CoNLL-2007 shared task. This robust-ness led to the third best overall average la-beled attachment score in the task, despite using no discriminative methods. We also demonstrate that the parser is quite fast, and can provide even faster parsing times with-out much loss of accuracy. 1