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Found 2 papers, 2 papers with code
AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced Datasets
By dynamically selecting different anchors at each iteration it promotes class balance and prevents overfitting the initial decision boundary, thus promoting the discovery of new clusters of minority instances.
Diable: Efficient Dialogue State Tracking as Operations on Tables
Sequence-to-sequence state-of-the-art systems for dialogue state tracking (DST) use the full dialogue history as input, represent the current state as a list with all the slots, and generate the entire state from scratch at each dialogue turn.
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