@inproceedings{zhang-etal-2022-allsh,
title = "{ALLSH}: Active Learning Guided by Local Sensitivity and Hardness",
author = "Zhang, Shujian and
Gong, Chengyue and
Liu, Xingchao and
He, Pengcheng and
Chen, Weizhu and
Zhou, Mingyuan",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.99",
doi = "10.18653/v1/2022.findings-naacl.99",
pages = "1328--1342",
abstract = "Active learning, which effectively collects informative unlabeled data for annotation, reduces the demand for labeled data. In this work, we propose to retrieve unlabeled samples with a local sensitivity and hardness-aware acquisition function. The proposed method generates data copies through local perturbations and selects data points whose predictive likelihoods diverge the most from their copies. We further empower our acquisition function by injecting the select-worst case perturbation. Our method achieves consistent gains over the commonly used active learning strategies in various classification tasks. Furthermore, we observe consistent improvements over the baselines on the study of prompt selection in prompt-based few-shot learning. These experiments demonstrate that our acquisition guided by local sensitivity and hardness can be effective and beneficial for many NLP tasks.",
}
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<abstract>Active learning, which effectively collects informative unlabeled data for annotation, reduces the demand for labeled data. In this work, we propose to retrieve unlabeled samples with a local sensitivity and hardness-aware acquisition function. The proposed method generates data copies through local perturbations and selects data points whose predictive likelihoods diverge the most from their copies. We further empower our acquisition function by injecting the select-worst case perturbation. Our method achieves consistent gains over the commonly used active learning strategies in various classification tasks. Furthermore, we observe consistent improvements over the baselines on the study of prompt selection in prompt-based few-shot learning. These experiments demonstrate that our acquisition guided by local sensitivity and hardness can be effective and beneficial for many NLP tasks.</abstract>
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%0 Conference Proceedings
%T ALLSH: Active Learning Guided by Local Sensitivity and Hardness
%A Zhang, Shujian
%A Gong, Chengyue
%A Liu, Xingchao
%A He, Pengcheng
%A Chen, Weizhu
%A Zhou, Mingyuan
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Findings of the Association for Computational Linguistics: NAACL 2022
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F zhang-etal-2022-allsh
%X Active learning, which effectively collects informative unlabeled data for annotation, reduces the demand for labeled data. In this work, we propose to retrieve unlabeled samples with a local sensitivity and hardness-aware acquisition function. The proposed method generates data copies through local perturbations and selects data points whose predictive likelihoods diverge the most from their copies. We further empower our acquisition function by injecting the select-worst case perturbation. Our method achieves consistent gains over the commonly used active learning strategies in various classification tasks. Furthermore, we observe consistent improvements over the baselines on the study of prompt selection in prompt-based few-shot learning. These experiments demonstrate that our acquisition guided by local sensitivity and hardness can be effective and beneficial for many NLP tasks.
%R 10.18653/v1/2022.findings-naacl.99
%U https://aclanthology.org/2022.findings-naacl.99
%U https://doi.org/10.18653/v1/2022.findings-naacl.99
%P 1328-1342
Markdown (Informal)
[ALLSH: Active Learning Guided by Local Sensitivity and Hardness](https://aclanthology.org/2022.findings-naacl.99) (Zhang et al., Findings 2022)
ACL
- Shujian Zhang, Chengyue Gong, Xingchao Liu, Pengcheng He, Weizhu Chen, and Mingyuan Zhou. 2022. ALLSH: Active Learning Guided by Local Sensitivity and Hardness. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 1328–1342, Seattle, United States. Association for Computational Linguistics.