  Visual features are a promising signal for learning bootstrap textual models.
However, blackbox learning models make it difficult to isolate the specific
contribution of visual components. In this analysis, we consider the case study
of the Visually Grounded Neural Syntax Learner (Shi et al., 2019), a recent
approach for learning syntax from a visual training signal. By constructing
simplified versions of the model, we isolate the core factors that yield the
model's strong performance. Contrary to what the model might be capable of
learning, we find significantly less expressive versions produce similar
predictions and perform just as well, or even better. We also find that a
simple lexical signal of noun concreteness plays the main role in the model's
predictions as opposed to more complex syntactic reasoning.
