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    <abstract>To alleviate human efforts from obtaining large-scale annotations, Semi-Supervised Relation Extraction methods aim to leverage unlabeled data in addition to learning from limited samples. Existing self-training methods suffer from the gradual drift problem, where noisy pseudo labels on unlabeled data are incorporated during training. To alleviate the noise in pseudo labels, we propose a method called MetaSRE, where a Relation Label Generation Network generates accurate quality assessment on pseudo labels by (meta) learning from the successful and failed attempts on Relation Classification Network as an additional meta-objective. To reduce the influence of noisy pseudo labels, MetaSRE adopts a pseudo label selection and exploitation scheme which assesses pseudo label quality on unlabeled samples and only exploits high-quality pseudo labels in a self-training fashion to incrementally augment labeled samples for both robustness and accuracy. Experimental results on two public datasets demonstrate the effectiveness of the proposed approach.</abstract>
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%0 Conference Proceedings
%T Semi-supervised Relation Extraction via Incremental Meta Self-Training
%A Hu, Xuming
%A Zhang, Chenwei
%A Ma, Fukun
%A Liu, Chenyao
%A Wen, Lijie
%A Yu, Philip S.
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F hu-etal-2021-semi-supervised
%X To alleviate human efforts from obtaining large-scale annotations, Semi-Supervised Relation Extraction methods aim to leverage unlabeled data in addition to learning from limited samples. Existing self-training methods suffer from the gradual drift problem, where noisy pseudo labels on unlabeled data are incorporated during training. To alleviate the noise in pseudo labels, we propose a method called MetaSRE, where a Relation Label Generation Network generates accurate quality assessment on pseudo labels by (meta) learning from the successful and failed attempts on Relation Classification Network as an additional meta-objective. To reduce the influence of noisy pseudo labels, MetaSRE adopts a pseudo label selection and exploitation scheme which assesses pseudo label quality on unlabeled samples and only exploits high-quality pseudo labels in a self-training fashion to incrementally augment labeled samples for both robustness and accuracy. Experimental results on two public datasets demonstrate the effectiveness of the proposed approach.
%R 10.18653/v1/2021.findings-emnlp.44
%U https://aclanthology.org/2021.findings-emnlp.44
%U https://doi.org/10.18653/v1/2021.findings-emnlp.44
%P 487-496

Markdown (Informal)

[Semi-supervised Relation Extraction via Incremental Meta Self-Training](https://aclanthology.org/2021.findings-emnlp.44) (Hu et al., Findings 2021)

• Semi-supervised Relation Extraction via Incremental Meta Self-Training (Hu et al., Findings 2021)

ACL

• Xuming Hu, Chenwei Zhang, Fukun Ma, Chenyao Liu, Lijie Wen, and Philip S. Yu. 2021. Semi-supervised Relation Extraction via Incremental Meta Self-Training. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 487–496, Punta Cana, Dominican Republic. Association for Computational Linguistics.