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

TL;DR

This paper introduces an active learning approach with human-in-the-loop to identify and re-annotate likely errors in noisy telephone conversation data, significantly improving Named Entity Recognition performance with minimal re-annotation.

Contribution

The study presents a novel active learning framework that efficiently detects annotation errors in noisy datasets, reducing re-annotation efforts and boosting NER accuracy.

Findings

Re-annotating about 6% of data improves F1 score by 25%.

Active learning effectively identifies high-error samples for correction.

Significant performance gains achieved with minimal re-annotation effort.

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%.