Toward Continual Learning for Conversational Agents
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While end-to-end neural conversation models have led to promising advances in reducing hand-crafted features and errors induced by the traditional complex system architecture, they typically require an enormous amount of data. Previous studies adopted a hybrid approach with knowledge-based components to abstract out domain-specific things or to augment data to cover more diverse patterns. On the contrary, we propose to directly address the problem using the recent development in the space of continual learning for neural models. Specifically, we adopt a domain-independent neural conversational model and introduce a novel neural continual learning algorithm that allows the conversational agent to accumulate skills across different tasks in a data-efficient way. To the best of our knowledge, this is the first work that applies continual learning for conversation systems. We verified the efficacy of our method through a conversational skill transfer from synthetic dialogs or human-human dialogs to human-computer conversations in a customer support domain.
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