Unimodal Intermediate Training for Multimodal Meme Sentiment Classification
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arXiv
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866908480417300480 |
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| author | Hazman, Muzhaffar McKeever, Susan Griffith, Josephine |
| author_facet | Hazman, Muzhaffar McKeever, Susan Griffith, Josephine |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2308_00528 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Unimodal Intermediate Training for Multimodal Meme Sentiment Classification Hazman, Muzhaffar McKeever, Susan Griffith, Josephine Computation and Language 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. |
| title | Unimodal Intermediate Training for Multimodal Meme Sentiment Classification |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2308.00528 |