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Telephone transcription data can be very noisy due to speech recognitionerrors, disfluencies, etc. Not only that annotating such data is verychallenging for the annotators, but also such data may have lots of annotationerrors even after the annotation job is completed, resulting in a very poormodel performance. In this paper, we present an active learning framework thatleverages human in the loop learning to identify data samples from theannotated dataset for re-annotation that are more likely to contain annotationerrors. In this way, we largely reduce the need for data re-annotation for thewhole dataset. We conduct extensive experiments with our proposed approach forNamed Entity Recognition and observe that by re-annotating only about 6%training instances out of the whole dataset, the F1 score for a certain entitytype can be significantly improved by about 25%.

Improving Named Entity Recognition in Telephone Conversations via Effective Active Learning with Human in the Loop