Structured Training for Neural Network Transition-Based Parsing

Understand

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.