Promoting Unified Generative Framework with Descriptive Prompts for Joint Multi-Intent Detection and Slot Filling

Abstract

Natural language understanding is a crucial aspect of task-oriented dialogue systems, encompassing intent detection (ID) and slot filling (SF). Conventional approaches for ID and SF solve the problems in a separate manners, while recent studies are now leaning toward joint modeling to tackle multi-intent detection and SF. Although the advancements in prompt learning offer a unified framework for ID and SF, current prompt-based methods fail to fully exploit the semantics of intent and slot labels. Additionally, the potential of using prompt learning to model the correlation between ID and SF in multi-intent scenarios remains unexplored. To address the issue, we propose a text-generative framework that unifies ID and SF. The prompt templates are constructed with label semantical descriptions. Moreover, we introduce an auxiliary task to explicitly capture the correlation between ID and SF. The experimental results on two benchmark datasets show that our method achieves an overall accuracy improvement of 0.4–1.5% in a full-data scenario and 1.4–2.7% in a few-shot setting compared with a prior method, establishing it as a new state-of-the-art approach.