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In Task-Oriented Dialogue (TOD) systems, correctly updating the system'sunderstanding of the user's needs (a.k.a dialogue state tracking) is key to asmooth interaction. Traditionally, TOD systems perform this update in threesteps: transcription of the user's utterance, semantic extraction of the keyconcepts, and contextualization with the previously identified concepts. Suchcascade approaches suffer from cascading errors and separate optimization.End-to-End approaches have been proved helpful up to the semantic extractionstep. This paper goes one step further paving the path towards completelyneural spoken dialogue state tracking by comparing three approaches: (1) astate of the art cascade approach, (2) a locally E2E approach with rule-basedcontextualization and (3) a completely neural approach.

Is one brick enough to break the wall of spoken dialogue state tracking?