Neural CRF Parsing

Understand

This paper describes a parsing model that combines the exact dynamic programming of CRF parsing with the rich nonlinear featurization of neural net approaches.

• Our model is structurally a CRF that factors over anchored rule productions, but instead of linear potential functions based on sparse features, we use nonlinear potentials computed via a feedforward neural network.
• Because potentials are still local to anchored rules, structured inference (CKY) is unchanged from the sparse case.
• Computing gradients during learning involves backpropagating an error signal formed from standard CRF sufficient statistics (expected rule counts).