Abstract
A spoken language understanding (SLU) system includes two main tasks, slot filling (SF)
and intent detection (ID). The joint model for
the two tasks is becoming a tendency in SLU.
But the bi-directional interrelated connections
between the intent and slots are not established
in the existing joint models. In this paper,
we propose a novel bi-directional interrelated
model for joint intent detection and slot filling. We introduce an SF-ID network to establish direct connections for the two tasks
to help them promote each other mutually.
Besides, we design an entirely new iteration
mechanism inside the SF-ID network to enhance the bi-directional interrelated connections. The experimental results show that the
relative improvement in the sentence-level semantic frame accuracy of our model is 3.79%
and 5.42% on ATIS and Snips datasets, respectively, compared to the state-of-the-art model.