Joint Intent Detection and Slot Filling with Wheel-Graph Attention Networks

Multiple deep learning-based joint models have demonstrated excellent results on Table 1: An example with intent and slot annotation the two tasks. In this paper, we propose a new joint (BIO format), which indicates the slot of movie name model with a wheel-graph attention network (Wheel-from an utterance with an intent PlayMusic. GAT) which is able to model interrelated connections directly for intent detection and slot filling. To construct a graph structure for utterances, we create intent The SLU module takesuser utterance as input and performs nodes, slot nodes, and directed edges. Intent nodes three tasks: domain determination, intent detection, can provide utterance-level semantic information for and slot filling [11]. Among them, the first two slot filling, while slot nodes can also provide local keyword tasks are often framed as a classification problem, which information for intent. Experiments show that infers the domain or intent (from a predefined set of our model outperforms multiple baselines on two public candidates) based on the current user utterance [27].