  A method for creating a vision-and-language (V&L) model is to extend a
language model through structural modifications and V&L pre-training. Such an
extension aims to make a V&L model inherit the capability of natural language
understanding (NLU) from the original language model. To see how well this is
achieved, we propose to evaluate V&L models using an NLU benchmark (GLUE). We
compare five V&L models, including single-stream and dual-stream models,
trained with the same pre-training. Dual-stream models, with their higher
modality independence achieved by approximately doubling the number of
parameters, are expected to preserve the NLU capability better. Our main
finding is that the dual-stream scores are not much different than the
single-stream scores, contrary to expectation. Further analysis shows that
pre-training causes the performance drop in NLU tasks with few exceptions.
These results suggest that adopting a single-stream structure and devising the
pre-training could be an effective method for improving the maintenance of
language knowledge in V&L extensions.
