@inproceedings{azime-etal-2023-masakhane,
    title = "Masakhane-Afrisenti at {S}em{E}val-2023 Task 12: Sentiment Analysis using {A}fro-centric Language Models and Adapters for Low-resource {A}frican Languages",
    author = "Azime, Israel Abebe  and
      Al-azzawi, Sana  and
      Tonja, Atnafu Lambebo  and
      Shode, Iyanuoluwa  and
      Alabi, Jesujoba  and
      Awokoya, Ayodele  and
      Oduwole, Mardiyyah  and
      Adewumi, Tosin  and
      Fanijo, Samuel  and
      Oyinkansola, Awosan",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.semeval-1.182",
    doi = "10.18653/v1/2023.semeval-1.182",
    pages = "1311--1316",
    abstract = "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",
}

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
Azime, Israel Abebe Al-azzawi, Sana Tonja, Atnafu Lambebo Shode, Iyanuoluwa Alabi, Jesujoba Awokoya, Ayodele Oduwole, Mardiyyah Adewumi, Tosin Fanijo, Samuel Oyinkansola, Awosan
Masakhane-Afrisenti at SemEval-2023 Task 12: Sentiment Analysis using Afro-centric Language Models and Adapters for Low-resource African Languages