Text Representation.
The model, implemented as a deep neural network, learns to respond by training on hundreds of millions context-reply $(c,r)$ pairs. First, similar to Henderson:2017arxiv, raw text from both $c$ and $r$ is converted to unigrams and bigrams. Photo Representation.
Photos are represented using convolutional neural net (CNN) models pretrained on ImageNet BIBREF17. We use a MobileNet model with a depth multiplier of 1.4, and an input dimension of $224 \times 224$ pixels as in BIBREF18. 
 Question: How does PolyResponse architecture look like?
Henderson:2017 MobileNet model