  Transformer models trained on massive text corpora have become the de facto
models for a wide range of natural language processing tasks. However, learning
effective word representations for function words remains challenging.
Multimodal learning, which visually grounds transformer models in imagery, can
overcome the challenges to some extent; however, there is still much work to be
done. In this study, we explore the extent to which visual grounding
facilitates the acquisition of function words using stable diffusion models
that employ multimodal models for text-to-image generation. Out of seven
categories of function words, along with numerous subcategories, we find that
stable diffusion models effectively model only a small fraction of function
words -- a few pronoun subcategories and relatives. We hope that our findings
will stimulate the development of new datasets and approaches that enable
multimodal models to learn better representations of function words.
