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Visual Dialog involves "understanding" the dialog history (what has beendiscussed previously) and the current question (what is asked), in addition togrounding information in the image, to generate the correct response. In thispaper, we show that co-attention models which explicitly encode dialog historyoutperform models that don't, achieving state-of-the-art performance (72 % NDCGon val set). However, we also expose shortcomings of the crowd-sourcing datasetcollection procedure by showing that history is indeed only required for asmall amount of the data and that the current evaluation metric encouragesgeneric replies. To that end, we propose a challenging subset (VisDialConv) ofthe VisDial val set and provide a benchmark of 63% NDCG.

History for Visual Dialog: Do we really need it?