On the other hand, go operates in a much more finite universe than conversation does. You have 2 pieces, and a number of places to put them. You have a goal.
Conversation doesn't work like that, and takes a vast amount of information to understand whether someone is being sarcastic.
Probably not, because the approach taken in the paper is identifying a small set of patterns that are present in sarcasm, not fully understanding the meaning and context of why the sentence is sarcastic. It's a useful tool with pragmatic applications in online discourse analysis, but not a solved problem.
It may appear to you to be the case, but DeepBlue used a tree search algorithm. AlphaGo was an ensemble method that was based on neural networks, just like how the researchers used to detect sarcasm (sequence to vector). Read the article!
There are some things easier for a computer to do, like lots of simple math, given that the computer can accurately store the result of each calculation, whereas people have difficulty doing so for 1000 integers.
And there are some things that are relatively easy for humans to do, like communicate with natural language. Sarcasm falls into the later category. This is because human understanding is heavily social and conceptual.
It’s like expecting AI to read handwriting, when people can’t even do that 100% of the time.