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Wit is a form of rich interaction that is often grounded in a specificsituation (e.g., a comment in response to an event). In this work, we attemptto build computational models that can produce witty descriptions for a givenimage. Inspired by a cognitive account of humor appreciation, we employlinguistic wordplay, specifically puns, in image descriptions. We develop twoapproaches which involve retrieving witty descriptions for a given image from alarge corpus of sentences, or generating them via an encoder-decoder neuralnetwork architecture. We compare our approach against meaningful baselineapproaches via human studies and show substantial improvements. We find thatwhen a human is subject to similar constraints as the model regarding wordusage and style, people vote the image descriptions generated by our model tobe slightly wittier than human-written witty descriptions. Unsurprisingly,humans are almost always wittier than the model when they are free to choosethe vocabulary, style, etc.

Punny Captions: Witty Wordplay in Image Descriptions