TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
Dependency Parsing	Penn Treebank	Andor et al.	POS	97.44	# 2
Dependency Parsing	Penn Treebank	Andor et al.	UAS	94.61	# 17
Dependency Parsing	Penn Treebank	Andor et al.	LAS	92.79	# 17

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Globally Normalized Transition-Based Neural Networks

ACL 2016 · Daniel Andor, Chris Alberti, David Weiss, Aliaksei Severyn, Alessandro Presta, Kuzman Ganchev, Slav Petrov, Michael Collins ·

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.