The <cite>MMD</cite> dataset <cite>(Saha et al., 2017)</cite> consists of 100/11/11k train/validation/test chat sessions comprising 3.5M context-response pairs for the model. Each session contains an average of 40 dialogue turns (average of 8 words per textual response, 4 images per image response). <cite>The data</cite> contains complex user queries, which pose new challenges for multimodal, task-based dialogue, such as quantitative inference (sorting, counting and filtering): "Show me more images of the 3rd product in some different directions", inference using domain knowledge and long term context: "Will the 5th result go well with a large sized messenger bag?", inference over aggregate of images: "List more in the upper material of the 5th image and style as the 3rd and the 5th", co-reference resolution.