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Online conversations include more than just text. Increasingly, image-basedresponses such as memes and animated gifs serve as culturally recognized andoften humorous responses in conversation. However, while NLP has broadened tomultimodal models, conversational dialog systems have largely focused only ongenerating text replies. Here, we introduce a new dataset of 1.56M text-gifconversation turns and introduce a new multimodal conversational model Pepe theKing Prawn for selecting gif-based replies. We demonstrate that our modelproduces relevant and high-quality gif responses and, in a large randomizedcontrol trial of multiple models replying to real users, we show that our modelreplies with gifs that are significantly better received by the community.

An animated picture says at least a thousand words: Selecting Gif-based Replies in Multimodal Dialog