@inproceedings{chang-etal-2023-nicenlp,
    title = "nice{NLP} at {S}em{E}val-2023 Task 10: Dual Model Alternate Pseudo-labeling Improves Your Predictions",
    author = "Chang, Yu  and
      Chen, Yuxi  and
      Zhang, Yanru",
    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://preview.aclanthology.org/moar-dois/2023.semeval-1.41/",
    doi = "10.18653/v1/2023.semeval-1.41",
    pages = "307--311",
    abstract = "Sexism is a growing online problem. It harms women who are targeted and makes online spaces inaccessible and unwelcoming. In this paper, we present our approach for Task A of SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS), which aims to perform binary sexism detection on textual content. To solve this task, we fine-tune the pre-trained model based on several popular natural language processing methods to improve the generalization ability in the face of different data. According to the experimental results, the effective combination of multiple methods enables our approach to achieve excellent performance gains."
}