Visually Grounded Language Learning: a review of language games,
  datasets, tasks, and models
Alessandro Suglia Ioannis Konstas Oliver Lemon
In recent years, several machine learning models have been proposed. They are
trained with a language modelling objective on large-scale text-only data. With
such pretraining, they can achieve impressive results on many Natural Language
Understanding and Generation tasks. However, many facets of meaning cannot be
learned by ``listening to the radio" only. In the literature, many
Vision+Language (V+L) tasks have been defined with the aim of creating models
that can ground symbols in the visual modality. In this work, we provide a
systematic literature review of several tasks and models proposed in the V+L
field. We rely on Wittgenstein's idea of `language games' to categorise such
tasks into 3 different families: 1) discriminative games, 2) generative games,
and 3) interactive games. Our analysis of the literature provides evidence that
future work should be focusing on interactive games where communication in
Natural Language is important to resolve ambiguities about object referents and
action plans and that physical embodiment is essential to understand the
semantics of situations and events. Overall, these represent key requirements
for developing grounded meanings in neural models.