HausaNLP at SemEval-2023 Task 10: Transfer Learning, Synthetic Data and Side-Information for Multi-Level Sexism Classification
On the map: Synthetic data and self-improvement for LLMs → LLM synthetic data generation
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in LLM synthetic data generation, one of 13 directions in Synthetic data and self-improvement for LLMs. Builds on 9 of its 13 references that are in the map. First posted on arXiv 28 Apr 2023.
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References · 9
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- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
- 2020HateBERT: Retraining BERT for Abusive Language Detection in Englishcited by 207
- 2021XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyondcited by 114
- 2023SemEval-2023 Task 10: Explainable Detection of Online Sexismcited by 48
- 2021SWSR: A Chinese Dataset and Lexicon for Online Sexism Detectioncited by 42
- 2022yosm: A new yoruba sentiment corpus for movie reviewscited by 14
- 2022HERDPhobia: A Dataset for Hate Speech against Fulani in Nigeriacited by 12
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