Kevin Duh not-so-recently asked me to write a "position piece" for the workshop he's co-organizing on semi-supervised learning in NLP. I not-so-recently agreed. And recently I actually wrote said position piece. You can also find a link off the workshop page. I hope people recognize that it's intentionally a bit tongue-in-cheek. If you want to discuss this stuff or related things in general, come to the panel at NAACL from 4:25 to 5:25 on 4 June at the workshop! You can read the paper for more information, but my basic point is that we can typically divide semi-supervised approached into one lump (semi-supervised) that work reasonably well with only labeled data and are just improved with unlabeled data and one lump (semi-unsupervised) that work reasonably well with only unlabeled data and are just improved with labeled data. The former are typically encode lots of prior information; the latter do not. Let's combine! (Okay, my claim is more nuanced than that, but that's the high-order bit.)
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