  We present a fast and scalable architecture called Explicit Modular
Decomposition (EMD), in which we incorporate both classification-based and
extraction-based methods and design four modules (for classification and
sequence labelling) to jointly extract dialogue states. Experimental results
based on the MultiWoz 2.0 dataset validates the superiority of our proposed
model in terms of both complexity and scalability when compared to the
state-of-the-art methods, especially in the scenario of multi-domain dialogues
entangled with many turns of utterances.
