A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding
This work addresses spoken language understanding for applications like voice assistants, but it is incremental as it builds on existing joint modeling approaches.
The authors tackled the problem of spoken language understanding by proposing a Stack-Propagation framework with token-level intent detection to better incorporate intent information for slot filling, achieving state-of-the-art performance and outperforming previous methods by a large margin on two public datasets.
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. In addition, to further alleviate the error propagation, we perform the token-level intent detection for the Stack-Propagation framework. Experiments on two publicly datasets show that our model achieves the state-of-the-art performance and outperforms other previous methods by a large margin. Finally, we use the Bidirectional Encoder Representation from Transformer (BERT) model in our framework, which further boost our performance in SLU task.