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

Saminu Mohammad Aliyu, Idris Abdulmumin, Shamsuddeen Hassan Muhammad, Ibrahim Said Ahmad, Saheed Abdullahi Salahudeen +2 more
4/28/2023
cs.CL

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Abstract

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 multi-level 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.

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Cite this paper

@article{aliyu2023hausanlp,
  title  = {HausaNLP at SemEval-2023 Task 10: Transfer Learning, Synthetic Data and Side-Information for Multi-Level Sexism Classification},
  author = {Saminu Mohammad Aliyu and Idris Abdulmumin and Shamsuddeen Hassan Muhammad and Ibrahim Said Ahmad and Saheed Abdullahi Salahudeen and Aliyu Yusuf and Falalu Ibrahim Lawan},
  year   = {2023},
  eprint = {2305.00076},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2305.00076v1}
}

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