Paper: Fast and Robust Multilingual Dependency Parsing with a Generative Latent Variable Model

ACL ID D07-1099
Title Fast and Robust Multilingual Dependency Parsing with a Generative Latent Variable Model
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2007
Authors

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.