Active Learning for New Domains in Natural Language Understanding

We explore active learning (AL) utterance selection for improving the accuracy of new underrepresented domains in a natural language understanding (NLU) system. Moreover, we propose an AL algorithm called Majority-CRF that uses an ensemble of classification and sequence labeling models to guide utterance selection for annotation. Experiments with three domains show that Majority-CRF achieves 6.6%-9% relative error rate reduction compared to random sampling with the same annotation budget, and statistically significant improvements compared to other AL approaches. Additionally, case studies with human-in-the-loop AL on six new domains show 4.6%-9% improvement on an existing NLU system.