  Unsupervised dependency parsing, which tries to discover linguistic
dependency structures from unannotated data, is a very challenging task. Almost
all previous work on this task focuses on learning generative models. In this
paper, we develop an unsupervised dependency parsing model based on the CRF
autoencoder. The encoder part of our model is discriminative and globally
normalized which allows us to use rich features as well as universal linguistic
priors. We propose an exact algorithm for parsing as well as a tractable
learning algorithm. We evaluated the performance of our model on eight
multilingual treebanks and found that our model achieved comparable performance
with state-of-the-art approaches.
