  Spoken language understanding (SLU), which is a core component of the
task-oriented dialogue system, has made substantial progress in the research of
single-turn dialogue. However, the performance in multi-turn dialogue is still
not satisfactory in the sense that the existing multi-turn SLU methods have low
portability and compatibility for other single-turn SLU models. Further,
existing multi-turn SLU methods do not exploit the historical predicted results
when predicting the current utterance, which wastes helpful information. To gap
those shortcomings, in this paper, we propose a novel Result-based Portable
Framework for SLU (RPFSLU). RPFSLU allows most existing single-turn SLU models
to obtain the contextual information from multi-turn dialogues and takes full
advantage of predicted results in the dialogue history during the current
prediction. Experimental results on the public dataset KVRET have shown that
all SLU models in baselines acquire enhancement by RPFSLU on multi-turn SLU
tasks.
