Incremental Learning from Scratch for Task-Oriented Dialogue Systems
Table 1: The number of normalized response candidates in each sub-dataset after entity replacement, both training and test data included.
['[EMPTY]', 'SubD1', 'SubD2', 'SubD3', 'SubD4', 'SubD5']
It is possible for the dialogue model to retrieve responses directly without any preprocessing. However, the fact that nearly all utterances contain entity information would lead to a slow model convergence. Thus, we replace all entities with the orders in which they appear in dialogues to normalize utterances. For example, if the $entity_9$ is the second distinct entity which appears in a dialogue, we rename it with $entity_order_2$ in the current episode. To better understand the behavior of our approach, we train IDS− on the SubD5 training set until 2,000 batches online updates are finished, and then freeze the model parameters and test it on the SubD5 test set. Among these responses, we pick four of them and draw their context embedding vectors. In each figure, the red dots are contexts responded by IDS− with high confidence, while the blue dots are contexts responded by human where there is low confidence.