  Intent detection and slot filling are two fundamental tasks for building a
spoken language understanding (SLU) system. Multiple deep learning-based joint
models have demonstrated excellent results on the two tasks. In this paper, we
propose a new joint model with a wheel-graph attention network (Wheel-GAT)
which is able to model interrelated connections directly for intent detection
and slot filling. To construct a graph structure for utterances, we create
intent nodes, slot nodes, and directed edges. Intent nodes can provide
utterance-level semantic information for slot filling, while slot nodes can
also provide local keyword information for intent. Experiments show that our
model outperforms multiple baselines on two public datasets. Besides, we also
demonstrate that using Bidirectional Encoder Representation from Transformer
(BERT) model further boosts the performance in the SLU task.
