An End-to-End Dialogue State Tracking System withMachine Reading Comprehension and Wide & Deep Classification
Table 6: The performance of our proposed system on the test dataset
['[EMPTY]', 'Joint Goal Accuracy']
We have done some limited ablation studies on the test dataset. The ensemble system using MRC-DST and WD-DST incorporating text augmentation achieves the best results, with more than 10% improvement over other methods. This is because feature engineering is not limited by the length of dialogue history and many language features can be constructed artificially. In the experiment, we find that the MRC-DST has a strong semantic understanding ability. We also use deep learning semantic representation to increase the generalization ability for WD-DST. The optimal result can be achieved by combining the data augmentation technology. The experiment results show that data augmentation can improve the performance in the zero-shot services, which is consistent with our observation in the development dataset.