HausaNLP at SemEval-2023 Task 10: Transfer Learning, Synthetic Data and Side-Information for Multi-Level Sexism Classification

• Main Authors: Aliyu, S.M., Abdulmumin, I., Muhammad, S.H., Ahmad, I.S., Salahudeen, S.A., Yusuf, A., Lawan, F.I.
• Format: Conference or Workshop Item
• Published: Association for Computational Linguistics 2023
• Online Access: http://scholars.utp.edu.my/id/eprint/38026/; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175400718&partnerID=40&md5=50bd07eed227e02d3ae47e0fe7e50f81

We present the findings of our participation in the SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS) task, a shared task on offensive language (sexism) detection on English Gab and Reddit dataset. We investigated the effects of transferring two language models: XLM-T (sentiment classification) and HateBERT (same domain - Reddit) for multilevel classification into Sexist or not Sexist, and other subsequent sub-classifications of the sexist data. We also use synthetic classification of unlabelled dataset and intermediary class information to maximize the performance of our models. We submitted a system in Task A, and it ranked 49th with F1-score of 0.82. This result showed to be competitive as it only under-performed the best system by 0.052 F1-score. © 2023 Association for Computational Linguistics.