@inproceedings{ye-etal-2020-zero,
title = "Zero-shot Text Classification via Reinforced Self-training",
author = "Ye, Zhiquan and
Geng, Yuxia and
Chen, Jiaoyan and
Chen, Jingmin and
Xu, Xiaoxiao and
Zheng, SuHang and
Wang, Feng and
Zhang, Jun and
Chen, Huajun",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.272/",
doi = "10.18653/v1/2020.acl-main.272",
pages = "3014--3024",
abstract = "Zero-shot learning has been a tough problem since no labeled data is available for unseen classes during training, especially for classes with low similarity. In this situation, transferring from seen classes to unseen classes is extremely hard. To tackle this problem, in this paper we propose a self-training based method to efficiently leverage unlabeled data. Traditional self-training methods use fixed heuristics to select instances from unlabeled data, whose performance varies among different datasets. We propose a reinforcement learning framework to learn data selection strategy automatically and provide more reliable selection. Experimental results on both benchmarks and a real-world e-commerce dataset show that our approach significantly outperforms previous methods in zero-shot text classification"
}
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<abstract>Zero-shot learning has been a tough problem since no labeled data is available for unseen classes during training, especially for classes with low similarity. In this situation, transferring from seen classes to unseen classes is extremely hard. To tackle this problem, in this paper we propose a self-training based method to efficiently leverage unlabeled data. Traditional self-training methods use fixed heuristics to select instances from unlabeled data, whose performance varies among different datasets. We propose a reinforcement learning framework to learn data selection strategy automatically and provide more reliable selection. Experimental results on both benchmarks and a real-world e-commerce dataset show that our approach significantly outperforms previous methods in zero-shot text classification</abstract>
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%0 Conference Proceedings
%T Zero-shot Text Classification via Reinforced Self-training
%A Ye, Zhiquan
%A Geng, Yuxia
%A Chen, Jiaoyan
%A Chen, Jingmin
%A Xu, Xiaoxiao
%A Zheng, SuHang
%A Wang, Feng
%A Zhang, Jun
%A Chen, Huajun
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F ye-etal-2020-zero
%X Zero-shot learning has been a tough problem since no labeled data is available for unseen classes during training, especially for classes with low similarity. In this situation, transferring from seen classes to unseen classes is extremely hard. To tackle this problem, in this paper we propose a self-training based method to efficiently leverage unlabeled data. Traditional self-training methods use fixed heuristics to select instances from unlabeled data, whose performance varies among different datasets. We propose a reinforcement learning framework to learn data selection strategy automatically and provide more reliable selection. Experimental results on both benchmarks and a real-world e-commerce dataset show that our approach significantly outperforms previous methods in zero-shot text classification
%R 10.18653/v1/2020.acl-main.272
%U https://aclanthology.org/2020.acl-main.272/
%U https://doi.org/10.18653/v1/2020.acl-main.272
%P 3014-3024
Markdown (Informal)
[Zero-shot Text Classification via Reinforced Self-training](https://aclanthology.org/2020.acl-main.272/) (Ye et al., ACL 2020)
ACL
- Zhiquan Ye, Yuxia Geng, Jiaoyan Chen, Jingmin Chen, Xiaoxiao Xu, SuHang Zheng, Feng Wang, Jun Zhang, and Huajun Chen. 2020. Zero-shot Text Classification via Reinforced Self-training. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3014–3024, Online. Association for Computational Linguistics.