Title
Finding Influential Instances for Distantly Supervised Relation Extraction.
Abstract
Distant supervision (DS) is a strong way to expand the datasets for enhancing relation extraction (RE) models but often suffers from high label noise. Current works based on attention, reinforcement learning, or GAN are black-box models so they neither provide meaningful interpretation of sample selection in DS nor stability on different domains. On the contrary, this work proposes a novel model-agnostic instance sampling method for DS by influence function (IF), namely REIF. Our method identifies favorable/unfavorable instances in the bag based on IF, then does dynamic instance sampling. We design a fast influence sampling algorithm that reduces the computational complexity from
Year
Venue
DocType
2022
International Conference on Computational Linguistics
Conference
Volume
Citations 
PageRank 
Proceedings of the 29th International Conference on Computational Linguistics
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Zifeng Wang100.34
Rui Wen2205.13
Xi Chen3358.36
Shao-Lun Huang45623.09
Ningyu Zhang56318.56
Yefeng Zheng61391114.67