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

11/27/2020
by   Yekyung Kim, et al.
0

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.

READ FULL TEXT
research
07/25/2022

Exploiting Diversity of Unlabeled Data for Label-Efficient Semi-Supervised Active Learning

The availability of large labeled datasets is the key component for the ...
research
08/16/2018

Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study

Several recent papers investigate Active Learning (AL) for mitigating th...
research
09/08/2021

Active Learning by Acquiring Contrastive Examples

Common acquisition functions for active learning use either uncertainty ...
research
04/28/2021

Diversity-Aware Batch Active Learning for Dependency Parsing

While the predictive performance of modern statistical dependency parser...
research
02/24/2021

Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomography

Motivation: Cryo-Electron Tomography (cryo-ET) is a 3D bioimaging tool t...
research
05/26/2022

Deep Active Learning with Noise Stability

Uncertainty estimation for unlabeled data is crucial to active learning....
research
12/29/2021

Active Learning-Based Optimization of Scientific Experimental Design

Active learning (AL) is a machine learning algorithm that can achieve gr...

Please sign up or login with your details

Forgot password? Click here to reset