Neuro-Symbolic Constraint Programming for Structured Prediction
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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. The entire architecture can be trained in an end-to-end fashion. An empirical evaluation on handwritten equation recognition shows that Nester achieves better performance than both the neural network and the constrained structured predictor on their own, especially when training examples are scarce, while scaling to more complex problems than other neuro-programming approaches. Nester proves especially useful to reduce errors at the semantic level of the problem, which is particularly challenging for neural network architectures.Sub
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