Cross-relation Cross-bag Attention for Distantly-supervised Relation Extraction

Abstract

Distant supervision leverages knowledge bases to automatically label instances, thus allowing us to train relation extractor without human annotations. However, the generated training data typically contain massive noise, and may result in poor performances with the vanilla supervised learning. In this paper, we propose to conduct multi-instance learning with a novel Cross-relation Cross-bag Selective Attention (C${}^2$SA), which leads to noise-robust training for distant supervised relation extractor. Specifically, we employ the sentence-level selective attention to reduce the effect of noisy or mismatched sentences, while the correlation among relations were captured to improve the quality of attention weights. Moreover, instead of treating all entity-pairs equally, we try to pay more attention to entity-pairs with a higher quality. Similarly, we adopt the selective attention mechanism to achieve this goal. Experiments with two types of relation extractor demonstrate the superiority of the proposed approach over the state-of-the-art, while further ablation studies verify our intuitions and demonstrate the effectiveness of our proposed two techniques.

AAAI 2019

References in corpus (4)

• Improving neural networks by preventing co-adaptation of feature detectors
• Classifying Relations by Ranking with Convolutional Neural Networks
• Learning with Noise: Enhance Distantly Supervised Relation Extraction with Dynamic Transition Matrix
• Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach

Cited by in corpus (1)

• From Bag of Sentences to Document: Distantly Supervised Relation Extraction via Machine Reading Comprehension