DRILL: Dynamic Representations for Imbalanced Lifelong Learning

Continual or lifelong learning has been a long-standing challenge in machine learning to date, especially in natural language processing (NLP). Although state-of-the-art language models such as BERT have ushered in a new era in this field due to their outstanding performance in multitask learning scenarios, they suffer from forgetting when being exposed to a continuous stream of non-stationary data. In this paper, we introduce DRILL, a novel lifelong learning architecture for open-domain sequence classification. DRILL leverages a biologically inspired self-organizing neural architecture to selectively gate latent language representations from BERT in a domain-incremental fashion. We demonstrate in our experiments that DRILL outperforms current methods in a realistic scenario of imbalanced classification from a data stream without prior knowledge about task or dataset boundaries. To the best of our knowledge, DRILL is the first of its kind to use a self-organizing neural architecture for open-domain lifelong learning in NLP.