This repository contains the baseline code for the WALNUT paper published in NAACL 2022. Detailed description about the data sets and methods can be manuscript at here.

Data for WALNUT can be downloaded from here.

document-level-baselines contains source codes for 5 baseline mthods (C, W, Snorkel, C+W, C+Sonrkel) for document level classification tasks;

document-level-GLC_MWNET_MLC contains source codes for 3 advanced semi-weakly sueprvised learning methods (GLC, MetaWN, MLC) for document-level classification tasks;

token-level-baselines contains source codes for 5 baseline mthods (C, W, Snorkel, C+W, C+Sonrkel) for token-level classification tasks;

token-level-GLC_MWNET_MLC contains source codes for 3 advanced semi-weakly sueprvised learning methods (GLC, MetaWN, MLC) for token-level classification tasks.

If you find WALNUT useful, please cite the following paper

@inproceedings{zheng2022walnut,
  title={WALNUT: A Benchmark on Semi-weakly Supervised Learning for Natural Language Understanding},
  author={Guoqing Zheng, Giannis Karamanolakis, Kai Shu, Ahmed Hassan Awadallah},
  booktitle={Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  year={2022}
}

This code repository is released under MIT License. (See LICENSE)