Papers › Structured Training for Neural Network Transition-Based Parsing
Structured Training for Neural Network Transition-Based Parsing
David Weiss, Chris Alberti, Michael Collins, Slav Petrov
We present structured perceptron training for neural network transition-based dependency parsing. We learn the neural network representation using a gold corpus augmented by a large number of automatically parsed sentences. Given this fixed network representation, we learn a final layer using the structured perceptron with beam-search decoding. On the Penn Treebank, our parser reaches 94.26% unlabeled and 92.41% labeled attachment accuracy, which to our knowledge is the best accuracy on Stanford Dependencies to date. We also provide in-depth ablative analysis to determine which aspects of our model provide the largest gains in accuracy.
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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 | Weiss et al. | LAS | 92.06 | #19 of 22 | Archive leaderboard | report |
| Dependency Parsing | Penn Treebank | Weiss et al. | POS | 97.3 | #19 of 22 | Archive leaderboard | report |
| Dependency Parsing | Penn Treebank | Weiss et al. | UAS | 94.01 | #19 of 22 | Archive leaderboard | report |
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