  We replicate a variation of the image captioning architecture by Vinyals et
al. (2015), then introduce dropout during inference mode to simulate the
effects of neurodegenerative diseases like Alzheimer's disease (AD) and
Wernicke's aphasia (WA). We evaluate the effects of dropout on language
production by measuring the KL-divergence of word frequency distributions and
other linguistic metrics as dropout is added. We find that the generated
sentences most closely approximate the word frequency distribution of the
training corpus when using a moderate dropout of 0.4 during inference.
