NUAA-QMUL at SemEval-2020 Task 8: Utilizing BERT and DenseNet for Internet Meme

NUAA-QMUL at SemEval-2020 Task 8: Utilizing BERT and DenseNet for Internet Meme Emotion Analysis

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.

NUAA-QMUL在SemEval-2020上的任务8：利用BERT和DenseNet进行Internet Meme情绪分析

本文介绍了我们对SemEval 2020任务8：运动分析的贡献。我们的系统从文本和图像中学习多模式嵌入，以便按情感对互联网模因进行分类。.. 我们的模型使用BERT学习文本嵌入，并使用DenseNet从图像中提取特征，然后通过串联将这两种特征结合在一起。我们还将我们的结果与DenseNet，ResNet，BERT和BERT-ResNet产生的结果进行比较。我们的结果表明，图像分类模型具有帮助对模因进行分类的潜力，而DenseNet的性能优于ResNet。但是，添加文本功能并不总是对Memotion Analysis有帮助。 （阅读更多）