论文标题
NUAA-QMUL在Semeval-2020任务8：利用Bert和Densenet进行互联网模因情绪分析
NUAA-QMUL at SemEval-2020 Task 8: Utilizing BERT and DenseNet for Internet Meme Emotion Analysis
论文作者
论文摘要
本文介绍了我们对Semeval 2020任务8：Memotion分析的贡献。我们的系统从文本和图像中学习多模式嵌入，以便通过情感对Internet模因进行分类。我们的模型使用BERT学习文本嵌入，并从带有Densenet的图像中提取功能，随后通过串联结合了这两个功能。我们还将我们的结果与Densenet，Resnet，Bert和Bert-Resnet产生的结果进行了比较。我们的结果表明，图像分类模型有可能用densenet优于重新系统来帮助对模因进行分类。但是，添加文本功能并不总是有助于审阅分析。
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.
