This code accompanies our EMNLP-2019 paper on the multi-label classification of accounts describing sexism suffered or witnessed.
Abstract:
Sexism, an injustice that subjects women and girls to enormous suffering, manifests in blatant as well as subtle ways. In the wake of growing documentation of experiences of sexism on the web, the automatic categorization of accounts of sexism has the potential to assist social scientists and policy makers in studying and countering sexism better. The existing work on sexism classification, which is different from sexism detection, has certain limitations in terms of the categories of sexism used and/or whether they can co-occur. To the best of our knowledge, this is the first work on the multi-label classification of sexism of any kind(s), and we contribute the largest dataset for sexism categorization. We develop a neural solution for this multi-label classification that can combine sentence representations obtained using models such as BERT with distributional and linguistic word embeddings using a flexible, hierarchical architecture involving recurrent components and optional convolutional ones. Further, we leverage unlabeled accounts of sexism to infuse domain-specific elements into our framework. The best proposed method outperforms several deep learning as well as traditional machine learning baselines by an appreciable margin.
If you use this code for any research, please cite:
Pulkit Parikh, Harika Abburi, Pinkesh Badjatiya, Radhika Krishnan, Niyati Chhaya, Manish Gupta, and Vasudeva Varma. 2019. Multi-label Categorization of Accounts of Sexism using a Neural Framework. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1642-1652
BibTeX:
@inproceedings{parikh-2019-multi, title={Multi-label Categorization of Accounts of Sexism using a Neural Framework}, author={Parikh, Pulkit and Abburi, Harika and Badjatiya, Pinkesh and Krishnan, Radhika and Chhaya, Niyati and Gupta, Manish and Varma, Vasudeva}, booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)}, pages={1642--1652}, year={2019}}
Please read our paper for acknowledgments/references pertaining to this code.