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.