@inproceedings{ru-etal-2020-active,
title = "Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space",
author = "Ru, Dongyu and
Feng, Jiangtao and
Qiu, Lin and
Zhou, Hao and
Wang, Mingxuan and
Zhang, Weinan and
Yu, Yong and
Li, Lei",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.441",
doi = "10.18653/v1/2020.findings-emnlp.441",
pages = "4908--4917",
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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<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.</abstract>
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%0 Conference Proceedings
%T Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space
%A Ru, Dongyu
%A Feng, Jiangtao
%A Qiu, Lin
%A Zhou, Hao
%A Wang, Mingxuan
%A Zhang, Weinan
%A Yu, Yong
%A Li, Lei
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F ru-etal-2020-active
%X 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.
%R 10.18653/v1/2020.findings-emnlp.441
%U https://aclanthology.org/2020.findings-emnlp.441
%U https://doi.org/10.18653/v1/2020.findings-emnlp.441
%P 4908-4917
Markdown (Informal)
[Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space](https://aclanthology.org/2020.findings-emnlp.441) (Ru et al., Findings 2020)
ACL