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

Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1983–1987 July 13-14, 2023 ©2023 Association for Computational Linguistics HausaNLP at SemEval-2023 Task 10: Transfer Learning, Synthetic Data and Side-Information for Multi-Level Sexism Classification Saminu Mohammad Aliyu1+, Idris Abdulmumin2+, Shamsuddeen Hassan Muhammad1+, Ibrahim Said Ahmad1+, Saheed Abdullahi Salahudeen3+, Aliyu Yusuf4, Falalu Ibrahim Lawan3+ 1Bayero University, Kano, 2Ahmad Bello University, Zaria, 3Kaduna State University, 4Universiti Teknologi PETRONAS, +HausaNLP smaliyu.cs@buk.edu.ng Abstract We present the findings of our participation in the SemEval-2023 Task 10: Explainable Detec- tion of Online Sexism (EDOS) task, a shared task on offensive language (sexism) detection on English Gab and Reddit dataset. We investi- gated the effects of transferring two language models: XLM-T (sentiment classification) and HateBERT (same domain - Reddit) for multi- level classification into Sexist or not Sexist, and other subsequent sub-classifications of the sex- ist 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. Content warning: All examples of sexism comments used are for illustrative purpose only. 1 Introduction Sexism is a form of written or verbal attack on women based on their gender and other identity (Kirk et al., 2023). In general, there is a global con- cern about the prevalence of hate on social media. Consequently, many studies have attempted to en- sure that the social media remain safe for everyone (Jiang et al., 2022). In this paper, we described our experiments for the SemEval-2023 Task 10: Ex- plainable Detection of Online Sexism (EDOS). The shared task was divided into 3 sub-tasks that are all aimed at predicting fine-grained information about the type of sexism that exists on social media sites of Gab and Reddit. More information is provided in Section 3 and the task description paper (Kirk et al., 2023). Our models were fine-tuned on two pre-trained language models, namely XLM-T (Barbieri et al., 2022) and HateBERT (Caselli et al., 2021). While XLM-T is a pretrained language model trained on 198 million tweets, the HateBERT on the other hand is a pretrained language model based on BERT (Devlin et al., 2019) that was pretrained on a large scale English Reddit dataset for the detec- tion of abusive language. We used only the dataset used by the task organizers in our experiment. See Kirk et al. (2023) for a description of the dataset. We made submission only to the Task A during the competition phase, and our system achieved a competitive performance of 0.82 F1-score. How- ever, we also described our attempts at creating competitive models for Tasks B and C. The main contributions of this paper are as fol- lows: 1. We investigated the effectiveness of transfer- ring two language models, namely, XLM-T and HateBERT for binary sexism classifica- tion. 2. We explore the use of synthetic classification of …

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