2022/09/14 by Yufang Liu, Ziyin Huang, Liu, Yufang +13
Computer Science · Mathematics · #Anomaly Detection Techniques and Applications #Artificial intelligence #Bootstrapping (finance) #Computation and Language (cs.CL) #Computer science #Data mining #Econometrics #FOS: Computer and information sciences #Garbage #Machine Learning and Data Classification #Machine learning #Mathematics #Noise reduction #Quality (philosophy) #Sample (material) #Selection (genetic algorithm) #Set (abstract data type) #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.2209.06596
openalex publication_date 2022/09/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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. It collects sample-level evidence for recognizing good instances (which is more informative than loss-level evidence). We also propose a teacher-student mechanism for controlling purity of intermediate results when bootstrapping the clean set. The whole approach is model-agnostic and demonstrates strong performances on both denoising real (NYT) and synthetic noisy datasets.