Rethinking Decoding in Multi-intent Spoken Language Understanding

Abstract

Multi-intent spoken language understanding (SLU) can handle multiple intent utterances in real-world scenarios, which has gained increasing research attention. Despite promising results achieved by existing joint models, they (1) perform utterance-level or token-level intent detection, resulting in suboptimal performance due to fixed thresholds or voting mechanisms; (2) incorporate all predicted intents into each slot hidden state and execute parallel slot decoding, which lacks precise intent-slot alignment and overlooks sequential dependencies between slot tokens. In this paper, we propose a new framework to tackle these two issues. For the first issue, we utilize a global pointer with auxiliary tasks to achieve span-based intent detection. For the second issue, we leverage span-based predicted intents for precise intent-slot guidance and introduce rotational position encoding in the interaction module to explicitly model sequential dependencies for precise slot filling. Experimental results on two benchmarks demonstrate the superiority of our framework.