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arXiv · 1603.06042

Globally Normalized Transition-Based Neural Networks

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

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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Daniel Andor, Chris Alberti, David Weiss, Aliaksei Severyn, Alessandro Presta, Kuzman Ganchev, Slav Petrov, Michael Collins. 2016-06-08. Globally Normalized Transition-Based Neural Networks. https://arxiv.org/abs/1603.06042

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