Multi-domain Dialogue State Tracking as Dynamic Knowledge Graph Enhanced Question Answering
Table 4: Joint accuracy on WOZ 2.0 dataset.
['Model', 'Joint Accuracy']
We compare with four published baselines. It also learns a joint model of all slots, enabling parameter sharing between slots. Unlike prior works that transfer knowledge between slots by sharing parameters, our model implicitly transfers knowledge by formulating each slot as a question and learning to answer all the questions. Our model has a 1.24% relative joint accuracy improvement over StateNet PSI. Although SUMBT achieves higher joint accuracy than DSTQA on WOZ 2.0 dataset, DSTQA achieves better performance than SUMBT on MultiWOZ 2.0 dataset, which is a more challenging dataset.