The dynamic nature of streaming data often introduces distribution shifts that challenge typical text classification models. This paper proposes an online learning framework tailored for streaming text classification under distribution shifts. First, we decompose a neural network-based text classification model into distinct modules and analyze the varying impact of updating these modules under different types of shifts. Based on this insight, we define three novel indicators to efficiently measure the extent of distribution shifts without evaluating the entire model. These indicators enable the development of predictive models that dynamically optimize module update strategies, balancing learning efficiency and accuracy in real-time. To the best of our knowledge, this is the first approach to systematically adapt model updates according to a trade-off between efficiency and accuracy in online text classification. Extensive experiments on real-world streaming datasets demonstrate the effectiveness of our method, which consistently outperforms both static update strategies and state-of-the-art online text classification models. Our code and data are publicly available athttps://github.com/bigbases/modular-online-adaptation.

Continual learning aims to progressively acquire new knowledge while retaining previously learned information, addressing the challenge of catastrophic forgetting. This paper introduces a novel continual learning method, called SPrint, which is the first research effort to devise the principles of self-paced learning for solving continual learning problems. SPrint dynamically adapts the complexity of samples for both new and previous tasks in response to the model’s current learning capacity. It employs a self-paced loss function for sampling new tasks and a forgetting occurrence for sampling previous tasks with replay memory. Through extensive empirical evaluation, we demonstrate that SPrint consistently outperforms state-of-the-art methods in various continual learning benchmarks. Our source code is publicly available at https://github.com/bigbases/SPrint.

This study presents an adaptive online learning method for text classification under distribution shifts. We formulate a typical neural network-based text classification model as multiple logical modules. By leveraging the characteristics of the modules, we introduce three novel indicators to effectively measure the degree of dynamic distribution shifts without evaluating the model. To enhance online learning, we tactically trade off between learning efficiency and accuracy based on distribution shifts measured in real time. To the best of our knowledge, this is the first effort to adapt the model to the preference of learning efficiency or accuracy for online text classification. Extensive experiments on real-world streaming text datasets show that our method outperforms the best static update strategy and state-of-the-art online text classification models. Our code and data are available at https://github.com/bigbases/online-learning-text.