5 ms·
From the paper: Good examples – cut republican billion will pay percent benefit cost – program fund educ cut provid health help million – economi job see n
by huac 11y ago
From the paper:
Good examples
– cut republican billion will pay percent benefit cost
– program fund educ cut provid health help million
– economi job see need percent continu import now
– job make work compani busi right american good
– iraq war us presid support vote administr congress
These don't really look that great to me (and the 'bad examples' are worse) but I'm not an NLP expert
- nicklo 11y agoFrom a technical standpoint, the paper is somewhat lacking. It's a standard Markov-chain generator with an additional prior defined on the cohesiveness of the generated text. I'm guessing all the press is due to it being US election season, but this is really no different from King James Programming (http://kingjamesprogramming.tumblr.com http://kingjamesprogramming.tumblr.com), which was entertaining, but nothing revolutionary.
- runn1ng 11y agoYep, Markov chain generators are always fun. I remember there being Markov chain generator for Hacker News, but it was a few months (maybe years) back.
- icebraining 11y agohttp://news.ycombniator.com http://news.ycombniator.com
- CPLX 11y ago"Interesting New Startup Developed Using The Restroom" was my favorite.
- cmdrfred 11y ago"$690 for an hour minimum wage for state management in haskell" I knew I should of learned Haskell!
- amyjess 11y ago"Color blind accessibility on Bootstrap and JQuery to make Vim more accessible" This made me physically cringe.
- pekk 11y agoThe real thing makes me physically cringe, so I would count that as a success.
- deleted 11y ago[deleted]
- TheOtherHobbes 11y agoThat's more interesting than great. But: SpaceX DSCOVR Launch – The Decision to Embrace Dinkiness is gold.
- deleted 11y ago[deleted]
- drostie 11y agoThe stuff that you're quoting is not the stuff that the `demo.py` script generates, but "latent dirichlet allocation" (whereas this project appears to generate based on a different alternate algorithm, the sentence-based one). The sentence-based one has a major flaw: it mostly "settles into" a given speech from its training set for a few paragraphs, then transitions to a paragraph from another speech whenever the statements become sufficiently generic. Another bug: it can sometimes lapse into an infinite loop. Here's the tail end of one of my runs: accordingly , the committee rose ; and the speaker pro tempore ( mrs. drake ) having assumed the chair , mr. gilchrest , acting chairman of the committee of jurisdiction would ask the counsel to explain it ; and if it is a drafting error , then that could be corrected . if it is not a drafting error , then that could be corrected . if it is not a drafting error , then that could be corrected . if it is not a drafting error , then that could be corrected . if it is not a drafting error , then that could be corrected . if it is not a drafting error , then that could be corrected . if it is not a drafting error , then that could be corrected . if it is not a drafting error , then that could be corrected . if it is not a drafting error , then that could be corrected . if it is not a drafting error , then that could be corrected . [... continues for many, many more lines before ending ...]
- deleted 11y ago[deleted]
- gkya 11y ago> Another bug: it can sometimes lapse into an infinite loop. That might be a bug that propagated from the politicians themselves...