Mardiyyah Oduwole
2023
Masakhane-Afrisenti at SemEval-2023 Task 12: Sentiment Analysis using Afro-centric Language Models and Adapters for Low-resource African Languages
Israel Abebe Azime
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Sana Al-azzawi
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Atnafu Lambebo Tonja
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Iyanuoluwa Shode
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Jesujoba Alabi
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Ayodele Awokoya
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Mardiyyah Oduwole
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Tosin Adewumi
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Samuel Fanijo
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Awosan Oyinkansola
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
Detecting harmful content on social media plat-forms is crucial in preventing the negative ef-fects these posts can have on social media users. This paper presents our methodology for tack-ling task 10 from SemEval23, which focuseson detecting and classifying online sexism insocial media posts. We constructed our solu-tion using an ensemble of transformer-basedmodels (that have been fine-tuned; BERTweet,RoBERTa, and DeBERTa). To alleviate the var-ious issues caused by the class imbalance inthe dataset provided and improve the general-ization of our model, our framework employsdata augmentation and semi-supervised learn-ing. Specifically, we use back-translation fordata augmentation in two scenarios: augment-ing the underrepresented class and augment-ing all classes. In this study, we analyze theimpact of these different strategies on the sys-tem’s overall performance and determine whichtechnique is the most effective. Extensive ex-periments demonstrate the efficacy of our ap-proach. For sub-task A, the system achievedan F1-score of 0.8613. The source code to re-produce the proposed solutions is available onGithub
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Co-authors
- Israel Abebe Azime 1
- Sana Al-Azzawi 1
- Atnafu Lambebo Tonja 1
- Iyanuoluwa Shode 1
- Jesujoba Alabi 1
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