A Scope Sensitive and Result Attentive Model for Multi-Intent Spoken Language Understanding. (arXiv:2211.12220v1 [cs.CL])

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 …

arxiv language spoken language understanding understanding