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

• URL: http://arxiv.org/abs/2011.02788v2
• Date: Mon, 9 Nov 2020 14:42:37 GMT
• Title: NUAA-QMUL at SemEval-2020 Task 8: Utilizing BERT and DenseNet for Internet Meme Emotion Analysis
• Authors: Xiaoyu Guo, Jing Ma, Arkaitz Zubiaga
• Abstract summary: Our system learns multi-modal embeddings from text and images in order to classify Internet memes by sentiment. Our results show that image classification models have the potential to help classifying memes, with DenseNet outperforming ResNet.
• Score: 18.86848589288164
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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