no code implementations • 5 May 2024 • Yuxi Xia, Anastasiia Sedova, Pedro Henrique Luz de Araujo, Vasiliki Kougia, Lisa Nußbaumer, Benjamin Roth
Finally, the prompt performance of detecting model memorization is quantified by the percentage of name pairs for which the model has higher confidence for the name from the training set.
1 code implementation • 7 Jun 2023 • Anastasiia Sedova, Lena Zellinger, Benjamin Roth
Instead of cleaning the dataset prior to model training, the dataset is dynamically adjusted during the training process.
1 code implementation • 10 May 2023 • Anastasiia Sedova, Benjamin Roth
In this paper, we attempt for the first time cold-start calibration for KGC, where no annotated examples exist initially for calibration, and only a limited number of tuples can be selected for annotation.
1 code implementation • 14 Apr 2022 • Anastasiia Sedova, Benjamin Roth
A cost-effective alternative to manual data labeling is weak supervision (WS), where data samples are automatically annotated using a predefined set of labeling functions (LFs), rule-based mechanisms that generate artificial labels for the associated classes.
1 code implementation • ACL (RepL4NLP) 2021 • Anastasiia Sedova, Andreas Stephan, Marina Speranskaya, Benjamin Roth
Strategies for improving the training and prediction quality of weakly supervised machine learning models vary in how much they are tailored to a specific task or integrated with a specific model architecture.