387 research outputs found
Conversational Agents, Humorous Act Construction, and Social Intelligence
Humans use humour to ease communication problems in human-human interaction and \ud
in a similar way humour can be used to solve communication problems that arise\ud
with human-computer interaction. We discuss the role of embodied conversational\ud
agents in human-computer interaction and we have observations on the generation\ud
of humorous acts and on the appropriateness of displaying them by embodied\ud
conversational agents in order to smoothen, when necessary, their interactions\ud
with a human partner. The humorous acts we consider are generated spontaneously.\ud
They are the product of an appraisal of the conversational situation and the\ud
possibility to generate a humorous act from the elements that make up this\ud
conversational situation, in particular the interaction history of the\ud
conversational partners
Is This a Joke? Detecting Humor in Spanish Tweets
While humor has been historically studied from a psychological, cognitive and
linguistic standpoint, its study from a computational perspective is an area
yet to be explored in Computational Linguistics. There exist some previous
works, but a characterization of humor that allows its automatic recognition
and generation is far from being specified. In this work we build a
crowdsourced corpus of labeled tweets, annotated according to its humor value,
letting the annotators subjectively decide which are humorous. A humor
classifier for Spanish tweets is assembled based on supervised learning,
reaching a precision of 84% and a recall of 69%.Comment: Preprint version, without referra
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
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