Globally Normalized Transition Based Neural Networks
We introduce a globally normalized transition based neural network model that achieves state of the art part of speech tagging, dependency parsing and sentence compression results. Our model is a simple feed forward neural network that operates on a task specific transition system, yet achieves comparable or better accuracies than recurrent models. We discuss the importance of global as opposed to local normalization: a key insight is that the label bias problem implies that globally normalized models can be strictly more expressive than locally normalized models.
ACL