Papers › Globally Normalized Transition-Based Neural Networks
Globally Normalized Transition-Based Neural Networks
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Dependency Parsing | Penn Treebank | Andor et al. | LAS | 92.79 | #17 of 22 | Archive leaderboard | report |
| Dependency Parsing | Penn Treebank | Andor et al. | POS | 97.44 | #17 of 22 | Archive leaderboard | report |
| Dependency Parsing | Penn Treebank | Andor et al. | UAS | 94.61 | #17 of 22 | Archive leaderboard | report |
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