  Multi-Intent Spoken Language Understanding (SLU), a novel and more complex
scenario of SLU, is attracting increasing attention. Unlike traditional SLU,
each intent in this scenario has its specific scope. Semantic information
outside the scope even hinders the prediction, which tremendously increases the
difficulty of intent detection. More seriously, guiding slot filling with these
inaccurate intent labels suffers error propagation problems, resulting in
unsatisfied overall performance. To solve these challenges, in this paper, we
propose a novel Scope-Sensitive Result Attention Network (SSRAN) based on
Transformer, which contains a Scope Recognizer (SR) and a Result Attention
Network (RAN). Scope Recognizer assignments scope information to each token,
reducing the distraction of out-of-scope tokens. Result Attention Network
effectively utilizes the bidirectional interaction between results of slot
filling and intent detection, mitigating the error propagation problem.
Experiments on two public datasets indicate that our model significantly
improves SLU performance (5.4\% and 2.1\% on Overall accuracy) over the
state-of-the-art baseline.
