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Knowing False Negatives: An Adversarial Training Method for Distantly Supervised Relation Extraction

2021-09-05 15:11:24
Kailong Hao, Botao Yu, Wei Hu

Abstract

Distantly supervised relation extraction (RE) automatically aligns unstructured text with relation instances in a knowledge base (KB). Due to the incompleteness of current KBs, sentences implying certain relations may be annotated as N/A instances, which causes the so-called false negative (FN) problem. Current RE methods usually overlook this problem, inducing improper biases in both training and testing procedures. To address this issue, we propose a two-stage approach. First, it finds out possible FN samples by heuristically leveraging the memory mechanism of deep neural networks. Then, it aligns those unlabeled data with the training data into a unified feature space by adversarial training to assign pseudo labels and further utilize the information contained in them. Experiments on two wildly-used benchmark datasets demonstrate the effectiveness of our approach.

Abstract (translated)

URL

https://arxiv.org/abs/2109.02099

PDF

https://arxiv.org/pdf/2109.02099.pdf


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