DC-Instruct: An Effective Framework for Generative Multi-intent Spoken Language Understanding
Bowen Xing, Lizi Liao, Minlie Huang, Ivor Wai-Hung Tsang · 2024
In the realm of multi-intent spoken language understanding, recent advancements have leveraged the potential of prompt learning frameworks.However, critical gaps exist in these frameworks: the lack of explicit modeling of dual-task dependencies and the oversight of task-specific semantic differences among utterances.To address these shortcomings, we propose DC-Instruct, a novel generative framework based on Dual-task Inter-dependent Instructions (DII) and Supervised Contrastive Instructions (SCI).Specifically, DII guides large language models (LLMs) to generate labels for one task based on the other task's labels, thereby explicitly capturing dual-task interdependencies.Moreover, SCI leverages utterance semantics differences by guiding LLMs to determine whether a pair of utterances share the same or similar labels.This can improve LLMs on extracting and discriminating task-specific semantics, thus enhancing their SLU reasoning abilities.Extensive experiments on public benchmark datasets show that DC-Instruct markedly outperforms current generative models and state-of-the-art methods, demonstrating its effectiveness in enhancing dialogue language understanding and reasoning.