Neuro-Symbolic Constraint Programming for Structured Prediction

Understand

We propose Nester, a method for injecting neural networks into constrained structured predictors.

• The job of the neural network(s) is to compute an initial, raw prediction that is compatible with the input data but does not necessarily satisfy the constraints.
• The structured predictor then builds a structure using a constraint solver that assembles and corrects the raw predictions in accordance with hard and soft constraints.
• In doing so, Nester takes advantage of the features of its two components: the neural network learns complex representations from low-level data while the constraint programming component reasons about the high-level properties of the prediction task.