A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding

Understand

Intent detection and slot filling are two main tasks for building a spoken language understanding (SLU) system.

• The two tasks are closely tied and the slots often highly depend on the intent.
• In this paper, we propose a novel framework for SLU to better incorporate the intent information, which further guides the slot filling.
• In our framework, we adopt a joint model with Stack-Propagation which can directly use the intent information as input for slot filling, thus to capture the intent semantic knowledge.