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This is a complete joke, right? What is better about those results than a simple "image + headline + random bottom line" algorithm?
This is a complete joke, right? What is better about those results than a simple "image + headline + random bottom line" algorithm?
They're called image macros, not memes.
This is a complete joke, right? What is better about those results than a simple "image + headline + random bottom line" algorithm?
Judging from the url posted in an earlier top thread, this might be a student report. https://web.stanford.edu/class/cs224n/reports/6909159.pdf
1) The input dataset from Memegenerator is a bit weird. More importantly, it does not distinctly identify top and bottom texts (some have a capital letter to signifify the start of the bottom text, which isn't always true). A good technique when encoding text for these types of things is to use a control token (e.g. a newline) to indicate these types of behaviors. (the conclusion notes this problem: "One example would be to train on a dataset that includes the break point in the text between upper and lower for the image. These were chosen manually here and are important for the humor impact of the meme.")
2) The use of GLoVe embeddings don't make as much sense here, even as a base. Generally the embeddings work best on text which follows real-world word usage, which memes do not follow. (in this case, it's better to let the network train the embeddings from scratch)
3) A 512-cell LSTM might be too big for a word-level model of that size; since the text follows rules, a 256-cell Bidirectional might work better.
It looks like a joke now but I'm fairly convinced that in the not too distant future the most influential social media accounts will be run by some kind of AI.
If not, I'll brb, need to set up some websites / facebook accounts.
Reminds me a bit of https://www.reddit.com/r/SubredditSimulator/