While neural conversational models have led to promising advances in reducing hand-crafted features and errors induced by the traditional complex system architecture, training neural models from scratch requires an enormous amount of data. If pre-trained models can be reused when they have many things in common with a new task, we can significantly cut down the amount of required data. To achieve this goal, we adopt a neural continual learning algorithm to allow a conversational agent to accumulate skills across different tasks in a data-efficient way.<\/p>\n","protected":false},"excerpt":{"rendered":"
While neural conversational models have led to promising advances in reducing hand-crafted features and errors induced by the traditional complex system architecture, training neural models from scratch requires an enormous amount of data. If pre-trained models can be reused when they have many things in common with a new task, we can significantly cut down 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