Abstract | ||
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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 Wang | 1 | 0 | 0.34 |
Rui Wen | 2 | 20 | 5.13 |
Xi Chen | 3 | 35 | 8.36 |
Shao-Lun Huang | 4 | 56 | 23.09 |
Ningyu Zhang | 5 | 63 | 18.56 |
Yefeng Zheng | 6 | 1391 | 114.67 |