STN4DST: A Scalable Dialogue State Tracking Based on Slot Tagging Navigation

Dialogue state tracking plays a key role in tracking user intentions in task-oriented dialogue systems. Traditional dialogue state tracking methods usually rely on selecting slot values from a fixed ontology to represent the dialogue state. In recent years, more flexible open vocabulary based approaches have become the mainstream focus which are mainly divided into two categories: generative methods and span extraction methods. Among them, the span extraction method is favored for its outstanding ability to predict unknown slot values. However, the span extraction method only focuses on the predicted slot values, but ignores other potential slot values in the utterance, which leads to insufficient semantic understanding of the utterance and difficulty in dealing with complex utterance scenarios, such as more or longer unknown slot values. To tackle the above drawbacks, in this paper, we propose a novel scalable dialogue state tracking method, which employs slot tagging to locate all potential slot values in the utterances and jointly learns slot pointers to select the predicted slot value from them. Specifically, our STN4DST (Slot Tagging Navigation for Dialogue State Tracking) model not only adopts the above joint learning strategy, which we call slot tagging navigation, to extract slot values from utterances, but also uses previous dialogue states as dialogue contexts to track the change of slot values, and introduces appendix slot values to predict special slot values that cannot be extracted. Extensive experiments show that in the open vocabulary setting, STN4DST achieves the state-of-the-art joint goal accuracy of 85.4% and 96.5% on Sim-M and Sim-R datasets with a large number of unknown slot values, and is also comparable to other state-of-the-art models in the absence of token-level slot annotations for all potential slot values.