A two-stage line graph reasoning framework for multi-intent spoken language understanding

Abstract

Spoken language understanding (SLU) is a core component of dialogue systems, responsible for intent detection and slot filling to interpret user utterances. We propose a two-stage line-graph-based framework for joint multi-intent SLU. In the first stage, our model uses cross-attention to encode token-level and intent-level features and leverages domain information to perform preliminary slot tagging and intent prediction. In the second stage, we construct an intent-slot graph from the first-stage outputs and then apply a line graph neural network to explicitly model interactions between each intent and its associated slots. This line graph reasoning mechanism reduces interference between multiple intents and enables effective bidirectional information flow, thereby enhancing semantic fusion of intent and slot cues. Experiments on the MixATIS and MixSNIPS benchmarks, as well as the challenging BlendATIS and BlendSNIPS datasets of blended utterances, demonstrate that the proposed approach outperforms state-of-the-art models, achieving higher slot-filling F₁, intent classification accuracy, and overall sentence-level understanding accuracy.