Who's to say what's funny? A computer using Language Models and Deep Learning, That's Who!
Humor is a defining characteristic of human beings. Our goal is to develop methods that automatically detect humorous statements and rank them on a continuous scale. In this paper we report on results using a Language Model approach, and outline our plans for using methods from Deep Learning.
Code (0)
등록된 구현이 없습니다.
Tasks
Language ModelingLanguage ModellingSimilar Papers 제목 키워드 기반
Neural Joking Machine : Humorous image captioning
What is an effective expression that draws laughter from human beings? In the present paper, in order to consider this question from an academic standpoint, we generate an image caption that draws a "laugh" by a computer…
Image CaptioningFunnyNet-W: Multimodal Learning of Funny Moments in Videos in the Wild
Automatically understanding funny moments (i.e., the moments that make people laugh) when watching comedy is challenging, as they relate to various features, such as body language, dialogues and culture. In this paper, w…
Language ModellingLarge Language ModelScene UnderstandingSpeech-to-TextFilling the Blanks (hint: plural noun) for Mad Libs Humor
Computerized generation of humor is a notoriously difficult AI problem. We develop an algorithm called Libitum that helps humans generate humor in a Mad Lib, which is a popular fill-in-the-blank game. The algorithm is ba…
"President Vows to Cut <Taxes> Hair": Dataset and Analysis of Creative Text Editing for Humorous Headlines
We introduce, release, and analyze a new dataset, called Humicroedit, for research in computational humor. Our publicly available data consists of regular English news headlines paired with versions of the same headlines…
``President Vows to Cut \textlessTaxes\textgreater Hair'': Dataset and Analysis of Creative Text Editing for Humorous Headlines
We introduce, release, and analyze a new dataset, called Humicroedit, for research in computational humor. Our publicly available data consists of regular English news headlines paired with versions of the same headlines…