2020/04/17 by Dongyu Ru, Ru, Dongyu, Jiangtao Feng +13 · 1 citation
Computer Science · #Active learning (machine learning) #Adversarial Robustness in Machine Learning #Adversarial system #Annotation #Artificial intelligence #Computation and Language (cs.CL) #Computer science #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms #Machine learning #Sample (material) #Sampling (signal processing) #Selection (genetic algorithm) #Sentence #Space (punctuation) #Speedup #Topic Modeling #cs.CL #cs.LG
paper · pdf · doi:10.48550/arxiv.2004.08046
published in arXiv (Cornell University) (Cornell University) · Accepted to EMNLP 2020 Findings
openalex publication_date 2020/04/17 · openalex created_date 2020/04/24 · arxiv created 2020/10/28 · arxiv updated 2020/10/29 · openalex updated_date 2026/07/28
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.