RFR: Representation-Focused Replay for Overcoming the Catastrophic Forgetting in Lifelong Language Learning

Zhongyuan Han, Zeyang Peng, Leilei Kong, Zhanhong Ye, Mingjie Huang, Haoliang Qi

Abstract: Replay-based approaches can combine with regularization-based approaches by introducing additional regularization terms or optimization constraints to alleviate catastrophic forgetting in lifelong language learning. The typical approach usually penalizes changes in the mapping function of a neural network. However, this constraint requires that the parameters of the whole network space are restricted when the model is learning the new tasks. It requires that the solution space of the model can only be near the original solution space, which limits the learning ability of the model on new data. To address this issue, we propose a novel approach called the Representation-Focused Replay (RFR) approach for lifelong language learning. RFR opts to prevent the changes of representations of replay examples while training with new data by introducing the differences in representations as the optimization constraint. Extensive experiments conducted on text classification benchmarks demonstrate the effectiveness of our proposed method. The experimental results show that RFR achieves an average accuracy of 78.2. Compared with the state-of-the-art baselines, RFR achieves higher accuracy on some task sequences and is close to the upper bound for the multi-task learning method.