Web: http://arxiv.org/abs/2209.06596

Sept. 15, 2022, 1:14 a.m. | Yufang Liu, Ziyin Huang, Yijun Wang, Changzhi Sun, Man Lan, Yuanbin Wu, Xiaofeng Mou, Ding Wang

cs.CL updates on arXiv.org arxiv.org

Existing distantly supervised relation extractors usually rely on noisy data
for both model training and evaluation, which may lead to
garbage-in-garbage-out systems. To alleviate the problem, we study whether a
small clean dataset could help improve the quality of distantly supervised
models. We show that besides getting a more convincing evaluation of models, a
small clean dataset also helps us to build more robust denoising models.
Specifically, we propose a new criterion for clean instance selection based on
influence functions. …

arxiv denoising

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