@inproceedings{guo-etal-2020-nuaa,
    title = "{NUAA}-{QMUL} at {S}em{E}val-2020 Task 8: Utilizing {BERT} and {D}ense{N}et for {I}nternet Meme Emotion Analysis",
    author = "Guo, Xiaoyu  and
      Ma, Jing  and
      Zubiaga, Arkaitz",
    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/fix-sig-urls/2020.semeval-1.114/",
    doi = "10.18653/v1/2020.semeval-1.114",
    pages = "901--907",
    abstract = "This paper describes our contribution to SemEval 2020 Task 8: Memotion Analysis. Our system learns multi-modal embeddings from text and images in order to classify Internet memes by sentiment. Our model learns text embeddings using BERT and extracts features from images with DenseNet, subsequently combining both features through concatenation. We also compare our results with those produced by DenseNet, ResNet, BERT, and BERT-ResNet. Our results show that image classification models have the potential to help classifying memes, with DenseNet outperforming ResNet. Adding text features is however not always helpful for Memotion Analysis."
}