Modular Model Adaptation for Online Learning in Streaming Text Classification

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.