Effective Utilization of External Knowledge and History Context in Multi-turn Spoken Language Understanding Model 1

At present, spoken language understanding (SLU) in multi-turn dialogue is a research hotspot, which mainly includes intent detection and slot filling. SLU models trained by large-scale corpus can learn good superficial semantic and grammatical information. But they lack the ability for modeling the knowledge needed to understand language. In order to further understand the deep semantic information of the dialogue, external knowledge needs to be modeled and incorporated into the SLU model. In addition, utilizing the correlation between history dialogue and current utterance is able to understand dialogue in multi-turn SLU. Thus, this paper proposes a joint model of intent detection and slot filling based on history context and external knowledge. This model constructs history dialogue encoder to obtain history context. Meanwhile, it constructs knowledge attention over context module. This module selects external knowledge according to the context information in current utterance and obtains knowledge representation. Finally, the history context and external knowledge representation are incorporated into the intent detection and slot filling joint model. The result of experiments on the common dataset demonstrate that with the help of external knowledge and history context, the performance of our model has a significant improvement.