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Intent detection and slot filling are two main tasks in natural languageunderstanding and play an essential role in task-oriented dialogue systems. Thejoint learning of both tasks can improve inference accuracy and is popular inrecent works. However, most joint models ignore the inference latency andcannot meet the need to deploy dialogue systems at the edge. In this paper, wepropose a Fast Attention Network (FAN) for joint intent detection and slotfilling tasks, guaranteeing both accuracy and latency. Specifically, weintroduce a clean and parameter-refined attention module to enhance theinformation exchange between intent and slot, improving semantic accuracy bymore than 2%. FAN can be implemented on different encoders and delivers moreaccurate models at every speed level. Our experiments on the Jetson Nanoplatform show that FAN inferences fifteen utterances per second with a smallaccuracy drop, showing its effectiveness and efficiency on edge devices.

A Fast Attention Network for Joint Intent Detection and Slot Filling on Edge Devices