  Being able to recognize words as slots and detect the intent of an utterance
has been a keen issue in natural language understanding. The existing works
either treat slot filling and intent detection separately in a pipeline manner,
or adopt joint models which sequentially label slots while summarizing the
utterance-level intent without explicitly preserving the hierarchical
relationship among words, slots, and intents. To exploit the semantic hierarchy
for effective modeling, we propose a capsule-based neural network model which
accomplishes slot filling and intent detection via a dynamic
routing-by-agreement schema. A re-routing schema is proposed to further
synergize the slot filling performance using the inferred intent
representation. Experiments on two real-world datasets show the effectiveness
of our model when compared with other alternative model architectures, as well
as existing natural language understanding services.
