Improving Named Entity Recognition in Telephone Conversations via Effective Active Learning with Human in the Loop
Abstract
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 to contain annotation errors. In this way, we largely reduce the need for data re-annotation for the whole dataset. We conduct extensive experiments with our proposed approach for Named Entity Recognition and observe that by re-annotating only about 6% training instances out of the whole dataset, the F1 score for a certain entity type can be significantly improved by about 25%.
Cite
@article{arxiv.2211.01354,
title = {Improving Named Entity Recognition in Telephone Conversations via Effective Active Learning with Human in the Loop},
author = {Md Tahmid Rahman Laskar and Cheng Chen and Xue-Yong Fu and Shashi Bhushan TN},
journal= {arXiv preprint arXiv:2211.01354},
year = {2022}
}
Comments
The final version of this paper will be published in the Proceedings of the DaSH Workshop @ EMNLP 2022. This paper is accepted for presentation in both DaSH@EMNLP 2022 and HiLL@NIPS 2022