SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling
This work addresses efficiency and accuracy challenges in spoken language understanding systems, offering a fast and improved solution for tasks like virtual assistants, though it is incremental as it builds on existing non-autoregressive approaches.
The paper tackled the problem of joint intent detection and slot filling in spoken language understanding by proposing SlotRefine, a non-autoregressive model with a two-pass iteration mechanism, which significantly outperformed previous models in slot filling and sped up decoding by up to 10.77 times.
Slot filling and intent detection are two main tasks in spoken language understanding (SLU) system. In this paper, we propose a novel non-autoregressive model named SlotRefine for joint intent detection and slot filling. Besides, we design a novel two-pass iteration mechanism to handle the uncoordinated slots problem caused by conditional independence of non-autoregressive model. Experiments demonstrate that our model significantly outperforms previous models in slot filling task, while considerably speeding up the decoding (up to X 10.77). In-depth analyses show that 1) pretraining schemes could further enhance our model; 2) two-pass mechanism indeed remedy the uncoordinated slots.