Unbalanced Class-incremental Learning for Text Classification Based on Experience Replay

Abstract

While deep learning has achieved remarkable results for text classification, incremental learning for text classification is still a challenge. The main problem is that models suffer from catastrophic forgetting, which is they always forget knowledge learned before when labelled data comes sequentially and is trained in sequence. In this study, we propose methods of preventing catastrophic forgetting to handle unbalanced increased data. As an improvement over experience replay, our approaches improve the accuracy about 23.3% with 23% of all training data on Yahoo and 9.5% with 12% of all training data and on DBPedia.