Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space
Abstract
Active learning for sentence understanding aims at discovering informative unlabeled data for annotation and therefore reducing the demand for labeled data. We argue that the typical uncertainty sampling method for active learning is time-consuming and can hardly work in real-time, which may lead to ineffective sample selection. We propose adversarial uncertainty sampling in discrete space (AUSDS) to retrieve informative unlabeled samples more efficiently. AUSDS maps sentences into latent space generated by the popular pre-trained language models, and discover informative unlabeled text samples for annotation via adversarial attack. The proposed approach is extremely efficient compared with traditional uncertainty sampling with more than 10x speedup. Experimental results on five datasets show that AUSDS outperforms strong baselines on effectiveness.
Cite
@article{arxiv.2004.08046,
title = {Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space},
author = {Dongyu Ru and Jiangtao Feng and Lin Qiu and Hao Zhou and Mingxuan Wang and Weinan Zhang and Yong Yu and Lei Li},
journal= {arXiv preprint arXiv:2004.08046},
year = {2020}
}
Comments
Accepted to EMNLP 2020 Findings