  Spoken Language Understanding (SLU), a core component of the task-oriented
dialogue system, expects a shorter inference facing the impatience of human
users. Existing work increases inference speed by designing non-autoregressive
models for single-turn SLU tasks but fails to apply to multi-turn SLU in
confronting the dialogue history. The intuitive idea is to concatenate all
historical utterances and utilize the non-autoregressive models directly.
However, this approach seriously misses the salient historical information and
suffers from the uncoordinated-slot problems. To overcome those shortcomings,
we propose a novel model for multi-turn SLU named Salient History Attention
with Layer-Refined Transformer (SHA-LRT), which composes of an SHA module, a
Layer-Refined Mechanism (LRM), and a Slot Label Generation (SLG) task. SHA
captures salient historical information for the current dialogue from both
historical utterances and results via a well-designed history-attention
mechanism. LRM predicts preliminary SLU results from Transformer's middle
states and utilizes them to guide the final prediction, and SLG obtains the
sequential dependency information for the non-autoregressive encoder.
Experiments on public datasets indicate that our model significantly improves
multi-turn SLU performance (17.5% on Overall) with accelerating (nearly 15
times) the inference process over the state-of-the-art baseline as well as
effective on the single-turn SLU tasks.
