@inproceedings{feely-etal-2023-qcon,
    title = "{QC}on at {S}em{E}val-2023 Task 10: Data Augmentation and Model Ensembling for Detection of Online Sexism",
    author = "Feely, Weston  and
      Gupta, Prabhakar  and
      Mohanty, Manas Ranjan  and
      Chon, Timothy  and
      Kundu, Tuhin  and
      Singh, Vijit  and
      Atluri, Sandeep  and
      Roosta, Tanya  and
      Ghaderi, Viviane  and
      Schulam, Peter",
    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-1.175",
    doi = "10.18653/v1/2023.semeval-1.175",
    pages = "1260--1270",
    abstract = "The web contains an abundance of user- generated content. While this content is useful for many applications, it poses many challenges due to the presence of offensive, biased, and overall toxic language. In this work, we present a system that identifies and classifies sexist content at different levels of granularity. Using transformer-based models, we explore the value of data augmentation, use of ensemble methods, and leverage in-context learning using foundation models to tackle the task. We evaluate the different components of our system both quantitatively and qualitatively. Our best systems achieve an F1 score of 0.84 for the binary classification task aiming to identify whether a given content is sexist or not and 0.64 and 0.47 for the two multi-class tasks that aim to identify the coarse and fine-grained types of sexism present in the given content respectively.",
}

The web contains an abundance of user- generated content. While this content is useful for many applications, it poses many challenges due to the presence of offensive, biased, and overall toxic language. In this work, we present a system that identifies and classifies sexist content at different levels of granularity. Using transformer-based models, we explore the value of data augmentation, use of ensemble methods, and leverage in-context learning using foundation models to tackle the task. We evaluate the different components of our system both quantitatively and qualitatively. Our best systems achieve an F1 score of 0.84 for the binary classification task aiming to identify whether a given content is sexist or not and 0.64 and 0.47 for the two multi-class tasks that aim to identify the coarse and fine-grained types of sexism present in the given content respectively.
Feely, Weston Gupta, Prabhakar Mohanty, Manas Ranjan Chon, Timothy Kundu, Tuhin Singh, Vijit Atluri, S eep Roosta, Tanya Ghaderi, Viviane Schulam, Peter
QCon at SemEval-2023 Task 10: Data Augmentation and Model Ensembling for Detection of Online Sexism