Structured prediction (SP) -- the machine learning task of generating outputs with complex internal structure -- is near and dear to my heart. It is also a generalization of many NLP problems (summarization, MT, IE, coref, parsing, tagging, etc.). This is the first part of a multi-part post on structured prediction (first: what's out there; second: what I do; third: where we should go).

The most important aspect of the SP problem (aside from the data) is the loss function: l(y,y') gives us how much it costs us to produce y' when the correct answer is y. Our goal, as always, is to minimize the expected loss over the distribution that generated our training data. The common way of doing this is to posit a large set Y of possible outputs and to try to find a function f(x,y) on an input x and output y that ranks outputs: f(x,y) > f(x,z) if y is a better output than z. Once we've learned such a function, the only thing left is to find a way to search over all y to find the one with highest score. Aside from the parameterization of f (which is almost always taken to be linear the feature set), the only thing left that matters is how we measure one f against another. For notational covenience, let g(x,y) = exp(-f(x,y)). Here are some common ways to score f:

- Generative: f is good if g(x,y) / sum_{x',y'} g(x',y') is high for all (x,y) in the training data
- Conditional: f is good if g(x,y) / sum_{y'} g(x,y') is high for all (x,y) in the training data
- Discriminative-M: f is good if f(x,y) > f(x,y') + l(y,y') for all (x,y) in the training data and all y', where errors are punished equally
- Discriminative-S: f is good if f(x,y) > f(x,y') for all (x,y) in the training data and all y', where errors are punished proportional to l(y,y').

Another way of looking at these methods is how we can optimize them. Let's suppose f is just w*h(x,y), where h(x) = sum_r h(x,r) is a decomposition over regions (adjacent tags in sequences, for instance), w is a weight vector and h provides a feature vector. We can compute the gradient of the optimization criterea (using exponentiated gradients for the discriminative methods) for each of the methods on a given input (x,y) as:

sum_{r in y} h(x,r) - sum_{y'} sum_{r' in y'} X h(x,r')

This is just the difference between the true features h(x,r) and the guessed features h(x,r'), where the guesses are weighed by X. The only difference between the above techniques is how we define X.

- Conditional: X = (1/Z) exp [ sum_{r' in y'} w*h(x,r') ]
- Discriminative-M: X = (1/Z) exp [ sum_{r' in y'} e C [ (t-1) l(y,r') + w*h(x,r') ] ]
- Discriminative-S: X = (1/Z) exp [ sum_{r' in y'} e C l(y,r')^(t-1) w*h(x,r') ]

What does this tell us? Well, it tells us exactly what we saw before: the gradient on the D-M model is high when the loss is high, even if the (non-loss-augmented) posterior is zero. This means that even if our model would never produce a wrong region, but that region has high loss, the gradient is still high at that point. This contrasts with the D-S gradient, which, once the posterior disappears, doesn't care about the loss anymore.

The only tricky thing in any of these models is to compute the sum over y' efficiently (or, alternatively, approximate it by computing the max over y'). In well formed cases, like sequence labeling or parsing (under strict assumptions), this is possible using standard dynamic programming (or sometimes, max-flow) methods.

Incidentally, the "conditional" model refers to CRFs, the "discriminative-s" model refers to SVMISOs and the "discriminative-m" model refers to M3Ns.

## 8 comments:

Appreciate the post on structured prediction! I would like to understand this better myself. :)

Regarding D-M vs. D-S: I thought the purpose of f(x,y)>f(x,y')+l(y,y') in D-M is to impose a margin of length l(y,y') on your f(). You don't want to just let your f() to give higher scores to (x,y) versus (x,y') in your training data; you also want to have a large margin so it generalizes well.

First, a minor comment: D-S also imposes a margin (I forgot to write it above). But it only imposes a constant margin of 1.

So the difference is that D-S scales the *slack variables* by the loss, while D-M scales the *margin* by the loss. To me, the former is much more intuitive.

Chanced upon your blog while surfing about Structured Data..here is something that you might find interesting( if you haven't seen it already)

http://nagoya.uchicago.edu/~dmcallester/colbounds.pdf

David proves some generalization bound theorems which suggest that M3Ns should be preferred over Hidden Markov SVMs..but the relationship is much more subtle. I haven't looked at D-S much, but I wonder how an approach like Least Squares will perform compared to the margin based approaches in this setting.

Looking forward to more discussion.

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