Scalable and Accurate Dialogue State Tracking via Hierarchical Sequence Generation
This addresses scalability issues in DST for multi-domain dialogues, offering a more efficient solution for dialogue systems.
The paper tackles the computational complexity of dialogue state tracking (DST) that scales with the number of pre-defined slots by proposing a hierarchical encoder-decoder generation framework without an ontology list, achieving state-of-the-art performance on multi-domain and single-domain datasets.
Existing approaches to dialogue state tracking rely on pre-defined ontologies consisting of a set of all possible slot types and values. Though such approaches exhibit promising performance on single-domain benchmarks, they suffer from computational complexity that increases proportionally to the number of pre-defined slots that need tracking. This issue becomes more severe when it comes to multi-domain dialogues which include larger numbers of slots. In this paper, we investigate how to approach DST using a generation framework without the pre-defined ontology list. Given each turn of user utterance and system response, we directly generate a sequence of belief states by applying a hierarchical encoder-decoder structure. In this way, the computational complexity of our model will be a constant regardless of the number of pre-defined slots. Experiments on both the multi-domain and the single domain dialogue state tracking dataset show that our model not only scales easily with the increasing number of pre-defined domains and slots but also reaches the state-of-the-art performance.