In this paper, we consider a practical setting that a batch 1 of new training data U t becomes available at each batch t. Our goal is to enable the continual learning capability of a slot filling model such that it can keep learning new utterances efficiently as well as remember old knowledge without the needs of accessing old training data.
unfortunately, existing approaches target on offline model training using a large scale training data.