Punny Captions: Witty Wordplay in Image Descriptions

Wit is a form of rich interaction that is often grounded in a specific situation (e.g., a comment in response to an event). In this work, we attempt to build computational models that can produce witty descriptions for a given image. Inspired by a cognitive account of humor appreciation, we employ linguistic wordplay, specifically puns, in image descriptions. We develop two approaches which involve retrieving witty descriptions for a given image from a large corpus of sentences, or generating them via an encoder-decoder neural network architecture. We compare our approach against meaningful baseline approaches via human studies and show substantial improvements. We find that when a human is subject to similar constraints as the model regarding word usage and style, people vote the image descriptions generated by our model to be slightly wittier than human-written witty descriptions. Unsurprisingly, humans are almost always wittier than the model when they are free to choose the vocabulary, style, etc.Comment: NAACL 2018 (11 pages

Worry, problem elaboration and suppression of imagery: the role of concreteness

Both lay concept and scientific theory claim that worry may be helpful for defining and analyzing problems. Recent studies, however, indicate that worrisome problem elaborations are less concrete than worry-free problem elaborations. This challenges the problem solving view of worry because abstract problem analyses are unlikely to lead to concrete problem solutions. Instead the findings support the avoidance theory of worry which claims that worry suppresses aversive imagery. Following research findings in the dual-coding framework [Paivio, A. (1971). Imagery and verbal processes. New York: Holt, Rhinehart and Winston; Paivio, A. (1986). Mental representations: a dual coding approach. New York: Oxford University Press.], the present article proposes that reduced concreteness may play a central role in the understanding of worry. First, reduced concreteness can explain how worry reduces imagery. Second, it offers an explanation why worrisome problem analyses are unlikely to arrive at solutions. Third, it provides a key for the understanding of worry maintenance

Optical tomography: Image improvement using mixed projection of parallel and fan beam modes

Mixed parallel and fan beam projection is a technique used to increase the quality images. This research focuses on enhancing the image quality in optical tomography. Image quality can be defined by measuring the Peak Signal to Noise Ratio (PSNR) and Normalized Mean Square Error (NMSE) parameters. The findings of this research prove that by combining parallel and fan beam projection, the image quality can be increased by more than 10%in terms of its PSNR value and more than 100% in terms of its NMSE value compared to a single parallel beam