@inproceedings{li-etal-2020-cn,
    title = "{CN}-{HIT}-{MI}.{T} at {S}em{E}val-2020 Task 8: Memotion Analysis Based on {BERT}",
    author = "Li, Zhen  and
      Zhang, Yaojie  and
      Xu, Bing  and
      Zhao, Tiejun",
    editor = "Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      May, Jonathan  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2020.semeval-1.145/",
    doi = "10.18653/v1/2020.semeval-1.145",
    pages = "1100--1105",
    abstract = "Internet memes emotion recognition is focused by many researchers. In this paper, we adopt BERT and ResNet for evaluation of detecting the emotions of Internet memes. We focus on solving the problem of data imbalance and data contains noise. We use RandAugment to enhance the data of the picture, and use Training Signal Annealing (TSA) to solve the impact of the imbalance of the label. At the same time, a new loss function is designed to ensure that the model is not affected by input noise which will improve the robustness of the model. We participated in sub-task a and our model based on BERT obtains 34.58{\%} macro F1 score, ranking 10/32."
}