@inproceedings{
cheng2024softlabel,
title={Soft-Label Integration for Robust Toxicity Classification},
author={Zelei Cheng and Xian Wu and Jiahao Yu and Shuo Han and Xin-Qiang Cai and Xinyu Xing},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=iYkhThIXG1}
}
Toxicity classification in textual content remains a significant problem. Data with labels from a single annotator fall short of capturing the diversity of human perspectives. Therefore, there is a growing need to incorporate crowdsourced annotations for training an effective toxicity classifier. Additionally, the standard approach to training a classifier using empirical risk minimization (ERM) may fail to address the potential shifts between the training set and testing set due to exploiting spurious correlations. This work introduces a novel bi-level optimization framework that integrates crowdsourced annotations with the soft-labeling technique and optimizes the soft-label weights by Group Distributionally Robust Optimization (GroupDRO) to enhance the robustness against out-of-distribution (OOD) risk. We theoretically prove the convergence of our bi-level optimization algorithm. Experimental results demonstrate that our approach outperforms existing baseline methods in terms of both average and worst-group accuracy, confirming its effectiveness in leveraging crowdsourced annotations to achieve more effective and robust toxicity classification.
Soft-Label Integration for Robust Toxicity Classification
Zelei Cheng Xian Wu Jiahao Yu Shuo Han Xin-Qiang Cai Xinyu Xing