Unimodal Intermediate Training for Multimodal Meme Sentiment
  Classification
Muzhaffar Hazman Susan McKeever Josephine Griffith
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.