[themadstork]

Hey, I'm the creator, and I agree that it's a bit hit or miss. I think the definitions from the words it generates on its own every 90 minutes (these are the ones without anyone's twitter handle at the bottom) are (on average) quite a bit better than those from the words people are tweeting at it, which it typically (and somewhat expectedly) has a bit more trouble with.

This bot was really just a creative experiment, and I'm pretty new to machine learning, so I'd love to hear any specific suggestions you might have to improve it.

[sdenton4]

The interesting pattern I noticed in the output is just lack of context in word roots. So you get a word that's clearly a portmanteau getting a completely unrelated definition.

Fortunately, breaking a word into parts is exactly the kind of thing a convolutional network should be great at...

[logicallee]

Well, I don't know what techniques you use or why you call it "deep learning". It seems even markov chains would tend to produce more sensible definitions, by mixing things that really do follow after one another. What were the techniques you used? Is there a write-up?

Note that I'm not a ML expert, just curious. I do have one specific advice: don't make up Greek and Latin stems - that seems like something you really can use deep learning for.

By the way the quality of the words themselves is pretty good - however you're generating these words/spellings, it seems pretty good.

[Houshalter]

It uses Char-RNN, see here: https://karpathy.github.io/2015/05/21/rnn-effectiveness/

It's far better than markov chains. Especially as markov chains have no memory. They just do a random walk through word space rather than form anything coherent.

[logicallee]

you say markov chains have no memory and are a randomwalk, but "a disease caused by a strong feeling of blurred and deceptive movements of the teeth" seems quite similar to me?

I think the markov chain that generates this hacker news simulator:

http://news.ycombniator.com/

Does CONSIDERABLY better in many cases.

Our discussion: https://news.ycombinator.com/item?id=10248773

Also I think this corporate bullshit generator uses markov chains: http://cbsg.sourceforge.net/cgi-bin/live and I also find many of its output examples to be superlative.

On the hacker news simulator, much of it is on-point: "Tell HN: Bump for Android in the UK on Monday" or "Ask HN: Any help to find short term, remote programming gig?"

Both of which are extremely intelligible, if surprising.

"The Illusion Machine That Changed Their Lives " makes perfect sense to me and I would 100% click.

As you can see from my link to our discussion, some people accepted the site as the genuine deal. (Obviously most titles it generates include clear give-aways: most, but not all.)

Given my review of its output I just think that the dictionary generating app we're discussing can use improvement.

[Houshalter]

That's because those titles are really short. And worse, it's not even generating new titles, but copying 2 existing titles and splicing them together in the middle. Which is cool, but this program creates every single letter from scratch, based on patterns it has learned from actual dictionaries.

Look at the "comments" of the ycombniator site. Where it tries to produce actual sentences. All the comments are totally incoherent and random.