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Existing approaches to lifelong language learning rely on plenty of labeleddata for learning a new task, which is hard to obtain in most real scenarios.Considering that humans can continually learn new tasks from a handful ofexamples, we expect the models also to be able to generalize well on newfew-shot tasks without forgetting the previous ones. In this work, we definethis more challenging yet practical problem as Lifelong Few-shot LanguageLearning (LFLL) and propose a unified framework for it based on prompt tuningof T5. Our framework called LFPT5 takes full advantage of PT's strong few-shotlearning ability, and simultaneously trains the model as a task solver and adata generator. Before learning a new domain of the same task type, LFPT5generates pseudo (labeled) samples of previously learned domains, and latergets trained on those samples to alleviate forgetting of previous knowledge asit learns the new domain. In addition, a KL divergence loss is minimized toachieve label consistency between the previous and the current model. Whileadapting to a new task type, LFPT5 includes and tunes additional promptembeddings for the new task. With extensive experiments, we demonstrate thatLFPT5 can be applied to various different types of tasks and significantlyoutperform previous methods in different LFLL settings.

LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5