  Intent classification and slot filling are two critical tasks for natural
language understanding. Traditionally the two tasks proceeded independently.
However, more recently joint models for intent classification and slot filling
have achieved state-of-the-art performance, and have proved that there exists a
strong relationship between the two tasks. In this paper, we propose a
bi-directional joint model for intent classification and slot filling, which
includes a multi-stage hierarchical process via BERT and bi-directional joint
natural language understanding mechanisms, including intent2slot and
slot2intent, to obtain mutual performance enhancement between intent
classification and slot filling. The evaluations show that our model achieves
state-of-the-art results on intent classification accuracy, slot filling F1,
and significantly improves sentence-level semantic frame accuracy when applied
to publicly available benchmark datasets, ATIS (88.6%) and SNIPS (92.8%).
