ELSF: Entity-Level Slot Filling Framework for Joint Multiple Intent Detection and Slot Filling

Zhanbiao Zhu, Peijie Huang, Haojing Huang, Yuhong Xu, Piyuan Lin, Leyi Lao, Shaoshen Chen, Haojie Xie, Shangjian Yin

Abstract: Multi-intent spoken language understanding (SLU) that can handle multiple intents in an utterance has attracted increasing attention. Previous studies treat the slot filling task as a token-level sequence labeling task, which results in a lack of entity-related information. In our paper, we propose an Entity-Level Slot Filling (ELSF) framework for joint multiple intent detection and slot filling. In our framework, two entity-oriented auxiliary tasks, entity boundary detection and entity type assignment, are introduced as the regularization to capture the entity boundary and the context of type, respectively. Besides, to better utilize the entity interaction, we design an effective entity-level coordination mechanism for modeling the interaction in both entity-entity and intent-entity relationships. Experiments on five datasets demonstrate the effectiveness and generalizability of our ELSF.