> generative grammar/ symbolic approaches were pretty much left behind by NN methods
Which is the same thing as hand-engineered feature stacks being left behind in vision problems, really. The story in every field is more or less "you're not clever enough to engineer good features"; "you might be clever enough to define good symmetries for the feature space in which the features live... maybe" (convolutional neural networks in image problems); "... but maybe not even that" (attention mechanisms).
The hand generated features are still superior for SfM style problems, where the geometry is well defined but would need to be learned by the NN from scratch.
Which is the same thing as hand-engineered feature stacks being left behind in vision problems, really. The story in every field is more or less "you're not clever enough to engineer good features"; "you might be clever enough to define good symmetries for the feature space in which the features live... maybe" (convolutional neural networks in image problems); "... but maybe not even that" (attention mechanisms).