<ul><li>BEAL is a Bayesian deep active learning method for efficient deep multi-label text classification.</li><li>It uses Bayesian deep learning with dropout to infer the model's posterior predictive distribution.</li><li>BEAL introduces an expected confidence-based acquisition function to select uncertain samples for annotation, reducing the need for labeled data.</li><li>Experimental results demonstrate that BEAL outperforms other active learning methods, achieving convergence with fewer labeled samples.</li></ul>

BEAL: A Bayesian Deep Active Learning Method for Efficient Deep Multi-Label Text Classification

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