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Globally Normalized Transition-Based Neural Networks

arXiv 1603.06042 ↗First posted 19 Mar 2016cs.CL cs.LG cs.NEcited by 26

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Abstract (arXiv)

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.

Key facts. Cited by 26 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Mar 2016.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 1; 2021: 10; 2022: 7; 2023: 4; 2024: 2; 2025: 0; 2026: 1.

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14 more
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  6. 2022Efficient and Robust Classification for Sparse Attackscited by 1
  7. 2022Algorithms for Weighted Pushdown Automatacited by 1
  8. 2023Exploring Energy-based Language Models with Different Architectures and Training Methods for Speech Recognitioncited by 1
  9. 2024HOLMES: to Detect Adversarial Examples with Multiple Detectorscited by 1
  10. 2024Stealthy Multi-Task Adversarial Attackscited by 1
  11. 2026Beyond Attack Success Rate: A Multi-Metric Evaluation of Adversarial Transferability in Medical Imaging Modelscited by 1
  12. 2021Adversarial Stein Training for Graph Energy Modelscited by 0
  13. 2022Towards a neural architecture of language: Deep learning versus logistics of access in neural architectures for compositional processingcited by 0
  14. 2023Globally Normalising the Transducer for Streaming Speech Recognitioncited by 0