  Spoken Language Understanding (SLU), a core component of the task-oriented
dialogue system, expects a shorter inference latency due to the impatience of
humans. Non-autoregressive SLU models clearly increase the inference speed but
suffer uncoordinated-slot problems caused by the lack of sequential dependency
information among each slot chunk. To gap this shortcoming, in this paper, we
propose a novel non-autoregressive SLU model named Layered-Refine Transformer,
which contains a Slot Label Generation (SLG) task and a Layered Refine
Mechanism (LRM). SLG is defined as generating the next slot label with the
token sequence and generated slot labels. With SLG, the non-autoregressive
model can efficiently obtain dependency information during training and spend
no extra time in inference. LRM predicts the preliminary SLU results from
Transformer's middle states and utilizes them to guide the final prediction.
Experiments on two public datasets indicate that our model significantly
improves SLU performance (1.5\% on Overall accuracy) while substantially speed
up (more than 10 times) the inference process over the state-of-the-art
baseline.
