Additional material for the publication
Hedderich, Zhu and Klakow:
Analysing the Noise Model Error for Realistic Noisy Label Data
AAAI 2021
This additional material is split into three parts:
- NoisyNER: You can find our newly proposed dataset for evaluating noisy-label settings in this separate repository https://github.com/uds-lsv/NoisyNER
- Noise Estimation Experiments: The code for the experiments comparing the theoretical, expected noise model error to the empirical measurements can be found in the subdirectory exp_noise_model_error.
- Base Model Performance: The code for the experiments showing the relationship between noise estimation and base model performance can be found in the subdirectory exp_base_model_performance.
Please refer to the README files in each directory for additional information on installation, reproduction, etc.
For more details, please refer to our publication https://arxiv.org/abs/2101.09763. If you have any questions or if you run into any issues, feel free to contact us.
When you work with this dataset, please consider citing us as
@inproceedings{hedderich2021analysing,
title={Analysing the Noise Model Error for Realistic Noisy Label Data},
author={Hedderich, Michael A and Zhu, Dawei and Klakow, Dietrich},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={35},
number={9},
pages={7675--7684},
year={2021}
}