  Spoken dialogue systems typically use a list of top-N ASR hypotheses for
inferring the semantic meaning and tracking the state of the dialogue. However
ASR graphs, such as confusion networks (confnets), provide a compact
representation of a richer hypothesis space than a top-N ASR list. In this
paper, we study the benefits of using confusion networks with a
state-of-the-art neural dialogue state tracker (DST). We encode the
2-dimensional confnet into a 1-dimensional sequence of embeddings using an
attentional confusion network encoder which can be used with any DST system.
Our confnet encoder is plugged into the state-of-the-art 'Global-locally
Self-Attentive Dialogue State Tacker' (GLAD) model for DST and obtains
significant improvements in both accuracy and inference time compared to using
top-N ASR hypotheses.
