The link to the actual paper is http://homepages.inf.ed.ac.uk/s0894589/petrovic13unsupervised.pdf
Two comments, one trivial and one more important:
trivial: personally, I don't think any of the jokes are really very funny. Then again, I never really liked the particular formula they're using, and I'm willing to accept that my taste in humour might be a little idiosyncratic.
important: I might be stating the obvious here, but I don't think it's really that enlightening to compare this with human joke-making, as I think they're mostly doing different things. Although the computer can do a passable job at filling in the variables for a given formula, that's a far cry from coming up with the formula in the first place. That's the impressive act of creativity, and after-the-fact mimicry of the same is much less impressive. When a computer gag program is flexible enough to come up with less stereotyped, formulaic gags, then I'll be impressed.
I'm reminded of, for example, EMI, Experiments In Musical Intelligence, David Cope's computer program capable of writing startlingly good pieces in the style of dead composers - but, of course, unable to come up with a distinctive, expressive style of its own.
That's the impressive act of creativity, and after-the-fact mimicry of the same is much less impressive.
This reminds me of the following articles, which probably don't reflect your intended sentiment, but might help some readers:
Nice. For what it's worth, my post's sentiment was pretty much wholescale cribbed from one or two of the essays in Hofstadter's book, Metamagical Themas.
There's another abstract on this topic here: http://acl2013.org/site/short/2316.html
I'm not sure how to find the actual paper link, though someone posted this one below.
I haven't read the article or the paper, but in fairness, humans are also pretty good at finding humor in things (there are older examples).
Use joke evaluator to filter output of joke generator. Bam! Massive improvement on joke hit rate.
"Our model significantly outperforms a competitive baseline and generates funny jokes 16% of the time, compared to 33% for human-generated jokes."
From this paper:
Unsupervised joke generation from big data
Sasa Petrovic and David Matthews
The 51st Annual Meeting of the Association for Computational Linguistics - Short Papers (ACL Short Papers 2013)
Sofia, Bulgaria, August 4-9, 2013
Abstract
Humor generation is a very hard problem. It is difficult to say exactly what makes a joke funny, and solving this problem algorithmically is assumed to require deep semantic understanding, as well as cultural and other contextual cues. We depart from previous work that tries to model this knowledge using ad-hoc manually created databases and labeled training examples. Instead we present a model that uses large amounts of unannotated data to generate I like my X like I like my Y, Z jokes, where X, Y, and Z are variables to be filled in. This is, to the best of our knowledge, the first fully unsupervised humor generation system. Our model significantly outperforms a competitive baseline and generates funny jokes 16% of the time, compared to 33% for human-generated jokes.
From The Register:
It uses 2,000,000 noun-adjective pairs of words to draw up jokes "with an element of surprise", something the creators claim is key to good comedy.
...
jokes calculated by the software include: