Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space

• URL: http://arxiv.org/abs/2004.08046v2
• Date: Wed, 28 Oct 2020 04:45:49 GMT
• Title: Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space
• Authors: Dongyu Ru, Jiangtao Feng, Lin Qiu, Hao Zhou, Mingxuan Wang, Weinan Zhang, Yong Yu, Lei Li
• Abstract summary: Active learning for sentence understanding aims at discovering informative unlabeled data for annotation. We argue that the typical uncertainty sampling method for active learning is time-consuming and can hardly work in real-time. We propose adversarial uncertainty sampling in discrete space (AUSDS) to retrieve informative unlabeled samples more efficiently.
• Score: 56.22272513894306
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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