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Unsupervised joke generation from big data

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21–30 of 72 posts

Re: Unsupervised joke generation from big data

#21
I am writing a bot to play Cards Against Humanity.

One trick to know if a sentence is funny or controversial that works quite well: Simply have the bot post it in an IRC channel or chat-room and count the number of responses it elicits (and give extra weight to words like "lol").

The more people fail the Turing-test, the funnier/controversial the statement.

Re: Unsupervised joke generation from big data

#22

I am writing a bot to play Cards Against Humanity. One trick to know if a sentence is funny or controversial that works quite well: Simply have the bot post it in an IRC channel or chat-room and count the number of responses it elicits (and give extra weight to words like "lol"). The more people fail the Turing-test, the funnier/controversial the statement.

what language are you writing the bot in?

Re: Unsupervised joke generation from big data

#24

I am writing a bot to play Cards Against Humanity. One trick to know if a sentence is funny or controversial that works quite well: Simply have the bot post it in an IRC channel or chat-room and count the number of responses it elicits (and give extra weight to words like "lol"). The more people fail the Turing-test, the funnier/controversial the statement.

what language are you writing the bot in?

Python. I'll open source when I am done.

Re: Unsupervised joke generation from big data

#25
post #13
post #9

Earlier quoted context omitted.

Their joke model misses one thing: each noun has to be commonly used in the phrase "I like my ". People often say "I like my coffee black" but nobody ever says "I like my source open." I think that is why they sound weird. But I like the off-kilter unexptectedness.

For jokes in this pattern, there's a somewhat more hard-coded twitter bot that I think does a better job: https://twitter.com/ilikelikeilike It does jokes of the form "I like my like I like my : , , [and not] ". noun1 is taken from a hand-coded list (hookups, fellas, lovers, spouses, etc.). noun2 is chosen randomly. Then the adjectives are just the top three adjectives found preceding noun2 in a corpus. So for exampl…

Two plugs in the same thread, come on!

Re: Unsupervised joke generation from big data

#26

I considered working on this once. Especially "comeback" generation. If you look at the comments (or replies to comments) on 9GAG, most of them are pretty generic and follow some basic rules. I figure even a computer can learn to make them.

Well, you certainly set your sights high.

Re: Unsupervised joke generation from big data

#28
The last time automated joke generation was discussed on HN, I learned that a large, or at least vocal, amount of people consider it to the be responsibility of anybody running automatic joke generation software to supervise the software to ensure that no truly offensive jokes are inadvertently generated.

I don't agree that such a responsibility exists, but it is probably worth keeping in mind that others do if you are interested in surfacing computer generated content like this. I would be wary of surfacing it without some form of auditing.

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