A data-selection method that expands from confirmed assistant errors via k-nearest neighbors in BiLSTM embedding spaces outperforms random and entropy-based active learning for domain classification.
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Active Learning for Domain Classification in a Commercial Spoken Personal Assistant
A data-selection method that expands from confirmed assistant errors via k-nearest neighbors in BiLSTM embedding spaces outperforms random and entropy-based active learning for domain classification.