Deep Active Learning for Sequence Labeling Based on Diversity and Uncertainty in Gradient

TL;DR

This paper proposes a novel active learning method for sequence labeling that combines uncertainty and diversity in gradient-based sampling, reducing labeled data needs across various NLP tasks.

Contribution

It introduces a sequence-based active learning approach integrating uncertainty and diversity in gradient embeddings, outperforming traditional methods.

Findings

Outperforms classic uncertainty-based sampling

Effective across multiple NLP tasks and datasets

Reduces labeled data requirements significantly

Abstract

Recently, several studies have investigated active learning (AL) for natural language processing tasks to alleviate data dependency. However, for query selection, most of these studies mainly rely on uncertainty-based sampling, which generally does not exploit the structural information of the unlabeled data. This leads to a sampling bias in the batch active learning setting, which selects several samples at once. In this work, we demonstrate that the amount of labeled training data can be reduced using active learning when it incorporates both uncertainty and diversity in the sequence labeling task. We examined the effects of our sequence-based approach by selecting weighted diverse in the gradient embedding approach across multiple tasks, datasets, models, and consistently outperform classic uncertainty-based sampling and diversity-based sampling.