An End-to-End Dialogue State Tracking System withMachine Reading Comprehension and Wide & Deep Classification
Table 2: Results of intent classification and requested slot model
['[EMPTY]', 'Development', 'Test']
The user intent recognition and user requested slots recognition are not core tasks in this competition. So we do not put many efforts into these two tasks, but only use a two-class RoBERTa-based classification model to solve them. We use the previous nine utterances together with each intent (or slot) description as the input to make a classification. The model has a maximum input length of 512, a batch size of 16, a learning rate of 2e-5, and a embedding dropout of 0.1. We only use the training dataset to train these models and choose the best model on the development dataset as the final model during ten epoch training. In the test dataset, the intent classification model achieves an average intent accuracy of 0.9482, and the requested slot model achieves an F1-score of 0.9847.