Context-Sensitive Generation Network for Handing Unknown Slot Values in Dialogue State Tracking
TABLE VI: The joint accuracy, slot accuracy and slot F1 of each model on the test set When 1% of the training set is used in individual restaurant domain on the MultiWOZ 2.1 dataset.
['[EMPTY]', 'Joint Acc', 'Slot Acc', 'Slot F1']
As we all know, in the case of insufficient training data, the model often faces a large number of USVs, which is a big obstacle to the generalization ability of the model. We can clearly find that when the model is faced with an extreme lack of training data, our model can relatively improve the generalization ability of the model. For the two types of DST models, the impact of our model is different: (1) For extractive DST, our proposed ”Enc” scheme (word contextual representation only) achieves the best results, which shows that when there is not enough training data to construct an adequate vocabulary space, the use of context to represent each word is more in line with the modeling requirements of the model for dialogue history distribution. (2) For hybrid DST, since the model needs to solve the vocabulary-based distribution and dialogue history distribution respectively and combine them when generating slot values, the ”Cat” scheme achieves better results. This is mainly due to the fact that the ”Cat” scheme combines the word vocabulary embedding and context representation, which provides more abundant information for solving the two distributions.