Unimodal Intermediate Training for Multimodal Meme Sentiment Classification

• URL: http://arxiv.org/abs/2308.00528v1
• Date: Tue, 1 Aug 2023 13:14:10 GMT
• Title: Unimodal Intermediate Training for Multimodal Meme Sentiment Classification
• Authors: Muzhaffar Hazman, Susan McKeever, Josephine Griffith
• Abstract summary: We present a novel variant of supervised intermediate training that uses relatively abundant sentiment-labelled unimodal data. Our results show a statistically significant performance improvement from the incorporation of unimodal text data. We show that the training set of labelled memes can be reduced by 40% without reducing the performance of the downstream model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Internet Memes remain a challenging form of user-generated content for automated sentiment classification. The availability of labelled memes is a barrier to developing sentiment classifiers of multimodal memes. To address the shortage of labelled memes, we propose to supplement the training of a multimodal meme classifier with unimodal (image-only and text-only) data. In this work, we present a novel variant of supervised intermediate training that uses relatively abundant sentiment-labelled unimodal data. Our results show a statistically significant performance improvement from the incorporation of unimodal text data. Furthermore, we show that the training set of labelled memes can be reduced by 40% without reducing the performance of the downstream model.

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