CoMAL: Contrastive Active Learning for Multi-Label Text Classification

Multi-label text classification (MLTC) allows a given text to be associated with multiple labels, which well suits many real-world data mining scenarios. However, the annotation effort of MLTC is inevitably expensive and time-consuming. Although multi-label active learning provides a cost-effective solution, it still faces two major challenges: (i) constructing decent feature space to distinguish the confusing semantics of different labels; (ii) defining proper sampling criteria to measure a sample's joint effect over the entire label space. To bridge these gaps, we propose a Contrastive Multi-label Active Learning framework (CoMAL) that gives an effective data acquisition strategy. Specifically, a contrastive decoupling mechanism is introduced to fully release the semantic information of multiple labels into the latent space. Then, we devise a hybrid criterion that balances two data value measures: (i) similarity-enhanced label cardinality inconsistency reflects the uncertainty of data predictions. (ii) positive feature diversity evaluates the positive-propensity semantic diversity to handle the label sparsity. Extensive experiments demonstrate that our CoMAL outperforms the current state-of-the-art multi-label active learning approaches. Code for CoMAL is available at https://github.com/chengzju/CoMAL.