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The Visual Dialogue task requires an agent to engage in a conversation aboutan image with a human. It represents an extension of the Visual QuestionAnswering task in that the agent needs to answer a question about an image, butit needs to do so in light of the previous dialogue that has taken place. Thekey challenge in Visual Dialogue is thus maintaining a consistent, and naturaldialogue while continuing to answer questions correctly. We present a novelapproach that combines Reinforcement Learning and Generative AdversarialNetworks (GANs) to generate more human-like responses to questions. The GANhelps overcome the relative paucity of training data, and the tendency of thetypical MLE-based approach to generate overly terse answers. Critically, theGAN is tightly integrated into the attention mechanism that generateshuman-interpretable reasons for each answer. This means that the discriminativemodel of the GAN has the task of assessing whether a candidate answer isgenerated by a human or not, given the provided reason. This is significantbecause it drives the generative model to produce high quality answers that arewell supported by the associated reasoning. The method also generates thestate-of-the-art results on the primary benchmark.

Are You Talking to Me? Reasoned Visual Dialog Generation through Adversarial Learning