Training with Exploration Improves a Greedy Stack-LSTM Parser

Understand

We adapt the greedy Stack-LSTM dependency parser of Dyer et al.

• (2015) to support a training-with-exploration procedure using dynamic oracles(Goldberg and Nivre, 2013) instead of cross-entropy minimization.
• This form of training, which accounts for model predictions at training time rather than assuming an error-free action history, improves parsing accuracies for both English and Chinese, obtaining very strong results for both languages.
• We discuss some modifications needed in order to get training with exploration to work well for a probabilistic neural-network.