Toward Continual Learning for Conversational Agents
Table 3: Experimental results on conversational skill transfer from human-human 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']
Now we turn to a more challenging problem — we bootstrap a conversation model by first learning from H-H dialogs without any manual labeling. We again compare three models trained by different training schemes: 1) No Transfer – we train a model on HC_reset_password from scratch, 2) Weight Transfer – we train a model on HH_reset_password, and continued to train the model on HC_reset_password 3) AEWC – the same as 2) except for AEWC being applied. The characteristics of H-H dialogs are vastly different from H-C dialogs. For example, the average utterance length and conversation length are way longer than H-C dialogs, introducing complex long dependencies. Also, H-H dialogs cover much broader topics that do not exactly match the conversation goal introducing many new vocabularies that do not occur in the corresponding H-C dialogs. In order to make the knowledge transfer process more robust to such non-trivial differences, the dropout regularization Srivastava et al. We also limited the maximum utterance length to 20 words. All three models work well on HC_reset_password. NT performs poorly on HC_reset_password+ as expected whereas WT interestingly shows a much higher performance than NT. This can be attributed to the fact that HH_reset_password contains a broader array of conversation skills including some knowledge on the target task compared to open_close, which leads to a more general and transferable model that would not necessarily be forgotten when faced with a new task. But the increasing performance gap between WT and AEWC on HC_reset_password+ clearly shows that WT gradually forgets the prior knowledge as it trains on more examples from a new task. Another noteworthy observation is that, when just one training example is given, the performances of WT and AEWC are much higher than that of NT. This result demonstrates that our method can capture meaningful knowledge from prior tasks in such a way that it partly works on a related new task.