Toward Continual Learning for Conversational Agents
Table 2: Experimental results on conversational skill transfer from synthetic to human-computer dialogs.
['Test Data Train SizeModel', 'HC_reset_password NT', 'HC_reset_password WT', 'HC_reset_password AEWC', 'HC_reset_password+ NT', 'HC_reset_password+ WT', 'HC_reset_password+ AEWC']
In order to clearly demonstrate the catastrophic forgetting problem, we compare three models trained by different training schemes: 1) No Transfer (NT) – we train a model on HC_reset_password from scratch, 2) Weight Transfer (WT) – we train a model on open_close, and continued to train the model on HC_reset_password, 3) AEWC – the same as 2) except for AEWC being applied. We compared accuracy on both the evaluation data from HC_reset_password and HC_reset_password+. All the models perform well on HC_reset_password due to the similarity between the training and evaluation data. But the performances of NT and WT on HC_reset_password+ significantly drop down. This surprisingly poor result confirms \newciteshalyminov2017challenging which found that neural conversation models can be badly affected by systemic noise. In this case, we systemically introduced unseen turns into dialogs. On the contrary, AWEC shows a higher performance than the others by trying to find optimal parameters not only for the previous task but also for the new task. One of key observations is that the performance of WT on HC_reset_password+ starts strong but keeps decreasing as more training examples are given. This indicates that weight transfer alone cannot help carry prior knowledge to a new task, rather it might lead to poor local optima if the prior knowlege is not general enough.