Improving Named Entity Recognition in Telephone Conversations via Effective Active Learning with Human in the Loop. (arXiv:2211.01354v1 [cs.CL])

Telephone transcription data can be very noisy due to speech recognition
errors, disfluencies, etc. Not only that annotating such data is very
challenging for the annotators, but also such data may have lots of annotation
errors even after the annotation job is completed, resulting in a very poor
model performance. In this paper, we present an active learning framework that
leverages human in the loop learning to identify data samples from the
annotated dataset for re-annotation that are more likely …

active learning arxiv conversations human human in the loop loop