  We present a new neural architecture for wide-coverage Natural Language
Understanding in Spoken Dialogue Systems. We develop a hierarchical multi-task
architecture, which delivers a multi-layer representation of sentence meaning
(i.e., Dialogue Acts and Frame-like structures). The architecture is a
hierarchy of self-attention mechanisms and BiLSTM encoders followed by CRF
tagging layers. We describe a variety of experiments, showing that our approach
obtains promising results on a dataset annotated with Dialogue Acts and Frame
Semantics. Moreover, we demonstrate its applicability to a different, publicly
available NLU dataset annotated with domain-specific intents and corresponding
semantic roles, providing overall performance higher than state-of-the-art
tools such as RASA, Dialogflow, LUIS, and Watson. For example, we show an
average 4.45% improvement in entity tagging F-score over Rasa, Dialogflow and
LUIS.
