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

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

URL: http://arxiv.org/abs/2011.13570v1
Date: Fri, 27 Nov 2020 06:03:27 GMT
Title: Deep Active Learning for Sequence Labeling Based on Diversity and Uncertainty in Gradient
Authors: Yekyung Kim
Abstract summary: We show 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.
Score: 5.33024001730262
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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