Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework

Junfan Chen, Richong Zhang, Yongyi Mao, Hongyu Guo, Jie Xu


Abstract
Distant supervision for relation extraction enables one to effectively acquire structured relations out of very large text corpora with less human efforts. Nevertheless, most of the prior-art models for such tasks assume that the given text can be noisy, but their corresponding labels are clean. Such unrealistic assumption is contradictory with the fact that the given labels are often noisy as well, thus leading to significant performance degradation of those models on real-world data. To cope with this challenge, we propose a novel label-denoising framework that combines neural network with probabilistic modelling, which naturally takes into account the noisy labels during learning. We empirically demonstrate that our approach significantly improves the current art in uncovering the ground-truth relation labels.
Anthology ID:
D19-1031
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
326–336
Language:
URL:
https://aclanthology.org/D19-1031
DOI:
10.18653/v1/D19-1031
Bibkey:
Cite (ACL):
Junfan Chen, Richong Zhang, Yongyi Mao, Hongyu Guo, and Jie Xu. 2019. Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 326–336, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework (Chen et al., EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-1031.pdf
Code
 AlbertChen1991/nEM