Joint Training Model of Intent Detection and Slot Filling for Multi Granularity Implicit Guidance
Bin Li, Weihua Wang, Feilong Bao · 2022
Spoken Language Understanding (SLU), which includes intent detection and slot filling, is an important module in task-based dialogue system. Most traditional models only take single intent and guide the slot filling explicitly. However, in a real conversation scenario, users may have more than one intentions in a sentence. In this paper, we propose an implicit guidance framework for joint intent detection and slot filling. We use token-level and sentence-level intent information to guide slot filling. The token level realizes fine-grained intent guidance, and the sentence level realizes coarse-grained intent guidance. The experimental results show that compared with the baseline model, our model improves the accuracy of sentence-level semantic framework on both multiple intents dataset MixSNIPS and single intent dataset SNIPS by 1.2% and 0.7%, respectively.