  Human conversation is a complex mechanism with subtle nuances. It is hence an
ambitious goal to develop artificial intelligence agents that can participate
fluently in a conversation. While we are still far from achieving this goal,
recent progress in visual question answering, image captioning, and visual
question generation shows that dialog systems may be realizable in the not too
distant future. To this end, a novel dataset was introduced recently and
encouraging results were demonstrated, particularly for question answering. In
this paper, we demonstrate a simple symmetric discriminative baseline, that can
be applied to both predicting an answer as well as predicting a question. We
show that this method performs on par with the state of the art, even memory
net based methods. In addition, for the first time on the visual dialog
dataset, we assess the performance of a system asking questions, and
demonstrate how visual dialog can be generated from discriminative question
generation and question answering.
