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. 
Daniel Andor, Chris Alberti, David Weiss, Aliaksei Severyn, Alessandro   Presta, Kuzman Ganchev, Slav Petrov and Michael Collins
cs.CL cs.LG cs.NE