Semi-supervised Relation Extraction via Incremental Meta Self-Training

Xuming Hu, Chenwei Zhang, Fukun Ma, Chenyao Liu, Lijie Wen, Philip S. Yu


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.
Anthology ID:
2021.findings-emnlp.44
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venue:
Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
487–496
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.44
DOI:
10.18653/v1/2021.findings-emnlp.44
Bibkey:
Cite (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.
Cite (Informal):
Semi-supervised Relation Extraction via Incremental Meta Self-Training (Hu et al., Findings 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-emnlp.44.pdf
Video:
 https://aclanthology.org/2021.findings-emnlp.44.mp4
Code
 THU-BPM/MetaSRE