The information about semantic similarity is held by d usr and d sys , which are fed to a non-linear layer to output a binary decision: where w d ∈ R 2L and b d are learnable parameters that map the semantic similarity to a belief state probability P t (d) of a domain d at a turn t. Slots and values are tracked using a similar architecture as for domain tracking (Figure 1).
to correctly model the context of the systemuser dialogue at each turn, three different cases are considered when computing the similarity vectors: 1.