A BI-Model Approach for Handling Unknown Slot Values in Dialogue State Tracking

Abstract

In this paper, we present an end-to-end bi-model structure for dialogue state tracking, which can handle the scenarios when the spoken language understanding model with a predefined slot candidate list is absent. Furthermore, the model structure described in this paper can effectively extract unknown slot values and still maintain the state-of-the-art performance on DSTC2 benchmark. We also compare our model in detail with an existing end-to-end dialogue state tracking model using pointer network which can also handle the unknown slot values, and demonstrates that how the bi-model structure can benefit the task and hence gives better performance.