Targeting the Source: Selective Data Curation for Debiasing NLP Models
Abstract
Unjustified social stereotypes have lately been found to taint the predictions of NLP models. Thus, an increasing amount of research focuses on developing methods to mitigate social bias. Most proposed approaches update the parameters of models post-hoc, running the risk of forgetting the predictive task of interest. In this work, we propose a novel way of debiasing NLP models by debiasing and curating their training data. To do so, we propose an unsupervised pipeline to identify which instances in the training data mention stereotypes that tally with the stereotypes encoded in NLP models. Then we either remove or augment these problematic instances, and train NLP models on less biased data. In this pipeline, we propose three methods to excavate stereotypes encoded in models using likelihoods, attention weights and vector representations. Experiments on the tasks of natural language inference, sentiment analysis and question answering suggest that our methods are better at debiasing downstream models than existing techniques.
Cite
Text
Gaci et al. "Targeting the Source: Selective Data Curation for Debiasing NLP Models." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2023. doi:10.1007/978-3-031-43415-0_17Markdown
[Gaci et al. "Targeting the Source: Selective Data Curation for Debiasing NLP Models." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2023.](https://mlanthology.org/ecmlpkdd/2023/gaci2023ecmlpkdd-targeting/) doi:10.1007/978-3-031-43415-0_17BibTeX
@inproceedings{gaci2023ecmlpkdd-targeting,
title = {{Targeting the Source: Selective Data Curation for Debiasing NLP Models}},
author = {Gaci, Yacine and Benatallah, Boualem and Casati, Fabio and Benabdeslem, Khalid},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2023},
pages = {276-294},
doi = {10.1007/978-3-031-43415-0_17},
url = {https://mlanthology.org/ecmlpkdd/2023/gaci2023ecmlpkdd-targeting/}
}