According to literature only neural dialoque state tracking systems were implemented in the past. The idea is to provide a dataset, for example from “Dialogue State Tracking Challenge” (DSTC), and use neural networks for memorizing the corpus. That means, the network has to create a hypothesis about the dialoque state, or to be more specific, it has to reproduce the known state given by the dataset. In theory, it is possible to extend the task by leaving out the neural network. Creating a Natural language processing pipeline doesn't need deeplearning, it can be done with normal expert systems and blackboard architectures too. The reason why this is seldom discussed in the literature, has to do with that such a system contains more than a single algorithm, but it's an integrated architecture. Similar to Question & Answering systems like “Apache UIMA” it contains of many submodules connected together. The direct answer to the OP is, that a dialogue tracking system only extends other modules like slot filling, it's not able to replace them. Slot filling means, to ground a spoken dialogue on the fly. While parsing the input stream, the RDF-triples are extracted for building the semantic model.