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Globally Normalized Transition-Based Neural Networks

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arxiv 1603.06042 v2 pith:QZA4OINJ submitted 2016-03-19 cs.CL cs.LGcs.NE

Daniel Andor , Chris Alberti , David Weiss , Aliaksei Severyn , Alessandro Presta , Kuzman Ganchev , Slav Petrov , Michael Collins This is my paper · ORCID
classification cs.CLcs.LGcs.NE
keywords normalizedgloballymodelsneuralachievesmodelnetworktransition-based
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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.

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