Small changes beget good or bad examples?

If you compare vision research with NLP research, there are a lot of interesting parallels. Like we both like linear models. And conditional random fields. And our problems are a lot harder than binary classification. And there are standard data sets that we've been evaluating on for decades and continue to evaluate on (I'm channeling Bob here :P).

But there's one thing that happens, the difference of which is so striking, that I'd like to call it to center stage. It has to do with "messing with our inputs."

I'll spend a bit more time describing the vision approach, since it's probably less familiar to the average reader. Suppose I'm trying to handwriting recognition to identify digits from zero to nine (aka MNIST). I get, say, 100 labeled zeros, 100 labeled ones, 100 labeled twos and so on. So a total of 1000 data points. I can train any off the shelf classifier based on pixel level features and get some reasonable performance (maybe 80s-90s, depending).

Now, I want to insert knowledge. The knowledge that I want to insert is some notion of invariance. I.e., if I take an image of a zero and translate it left a little bit, it's still a zero. Or up a little bit. Of if I scale it up 10%, it's still a zero. Or down 10%. Or if I rotate it five degrees. Or negative five. All zeros. Same hold for all the other digits.

One way to insert this knowledge is to muck with the learning algorithm. That's too complicated for me: I want something simpler. So what I'll do is take my 100 zeros and 100 ones and so on and just manipulate them a bit. That is, I'll sample a random zero, and apply some small random transformations to it, and call it another labeled example, also a zero. Now I have 100,000 training points. I train my off the shelf classifier based on pixel level features and get 99% accuracy or more. The same trick works for other vision problem (eg., recognizing animals). (This process is so common that it's actually described in Chris Bishop's new-ish PRML book!)

This is what I mean by small changes (to the input) begetting good example. A slightly transformed zero is still a zero.

Of course, you have to be careful. If you rotate a six by 180 degrees, you get a nine. If you rotate a cat by 180 degrees, you get an unhappy cat. More seriously, if you're brave, you might start looking at a class of transformations called diffeomorphisms, which are fairly popular around here. These are nice because of their nice mathematical properties, but un-nice because they can be slightly too flexible for certain problems.

Now, let's go over to NLP land. Do we ever futz with our inputs?

Sure!

In language modeling, we'll sometimes permute words or replace one word with another to get a negative example. Noah Smith futzed with his inputs in contrastive estimation to produce negative examples by swapping adjacent words, or deleting words.

In fact, try as I might, I cannot think of a single case in NLP where we make small changes to an input to get another good input: we always do it to get a bad input!

In a sense, this means that one thing that vision people have that we don't have is a notion of semantics preserving transformations. Sure, linguists (especially those from that C-guy) study transformations. And there's a vague sense that work in paraphrasing leads to transformations that maintain semantic equivalence. But the thing is that we really don't know any transformations that preserve semantics. Moreover, some transformations that seem benign (eg., passivization) actually are not: one of my favorite papers at NAACL this year by Greene and Resnik showed that syntactic structure affects sentiment (well, them, drawing on a lot of psycholinguistics work)!

I don't have a significant point to this story other than it's kind of weird. I mentioned this to some people at ICML and got a reaction that replacing words with synonyms should be fine. I remember doing this in high school, when word processors first started coming with thesauri packed in. The result seemed to be that if I actually knew the word I was plugging in, life was fine... but if not, it was usually a bad replacement. So this seems like something of a mixed bag: depending on how liberal you are with defining "synonym" you might be okay do this, but you might also not be.