Globally Normalized Transition-Based Neural Networks
2016
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.
Keywords:
- Correction
- Cite
- Save
- Machine Reading By IdeaReader
39
References
42
Citations
NaN
KQI