3 ms·

This is why I love it when I post a comment to HN and people can't tell if I'm being sarcastic or not. (https://news.ycombinator.com/item?id=12698590 https://ne

This is why I love it when I post a comment to HN and people can't tell if I'm being sarcastic or not. (https://news.ycombinator.com/item?id=12698590 https://news.ycombinator.com/item?id=12698590) I'm thinking in the future, an adversarial neural network could be used for just this purpose. Take my sarcastic comment and alter it just enough so that it appears non-sarcastic or vis versa. It's kind of like using neural style to mess with the face recognition algorithms of social media profile pictures.

webmaven 10y ago
This seems like a good match of methodology to the problem. Sarcasm seems rather adversarial by nature: the goal in delivery is often to signal, with intonation, only just enough to disambiguate intent. In highly competitive environments this results in deadpan delivery being common or even the default, and sarcasm having to be inferred entirely from content & context. (consider the opposite: delivery with an exaggerated sarcastic tone: It's actually insulting and rude regardless of the content.) People who haven't been exposed to such high sarcasm environments rely much more strongly on intonation, and correspondingly find it relatively difficult to identify sarcasm in text (again, this matches well with GAN failure modes). OTOH, I have personally been inadvertently fooled the opposite way, listening to a speaker with growing joy at his elaborate windup of the surrounding audience in a social setting, only to realize "holy shit, this guy is actually seriously a neo-nazi advocating eugenics. Back away slowly."