Explanatory Models

I am frequently asked the question: why does your application for solving XXX make such and such an error? (You can easily replace "your" with any possessive noun and the statement remains valid.)

My standard answer is to shrug and say "who knows."

This is quite different from, for instance, work in pattern matching for information extraction (many other citations are possible). In this setting, when the system makes an error, one can ask the system "what pattern caused this error." You can then trace the pattern back to the source documents from which it came and obtain some understanding for what is going on.

This is frequently not the case for your generic sequence labeling algorithm. If, say, a CRF misses a person name, what can you do about it? Can you understand why it made the error. More generally, if a model of any variety errs, can it say anything about why this error came to be.

One way to approach this problem would be to try to inspect the weights of the learned algorithm, but there are so many trade-offs going on internally that I've never been able to do this successfully (by hand, at least --- perhaps a clever tool could help, but I'm not sure). An alternative that I've been thinking about recently (but probably won't work on because I haven't enough time right now) is instead to pose the question as: what is the minimal change to the input required so that I would have made the decision correctly.

I guess one way to think about this is consider the case that a POS system misses tagging "Fred" in the sentence "Fred is not happy" as an NNP and instead calls it a "VBD." Presumably we have a bunch of window features about Fred that give us its identify, prefixes and suffixes, etc. Perhaps if "Fred" had been "Harry" this wouldn't have happened because "Fred" has the spelling feature "-ed." (Ok, clearly this is a very crafted example, but you get the idea.)

The question is: how do you define minimal change in features. If we're in an HMM (where we're willing to assume feature independence), then I don't think this is a big problem. But in the case where "Fred" ends in "-ed", it also ends in "-d" and both of these make it look more like a VBD. Such an explanatory system would ideally like to know that if "-d" weren't there, then neither would be "-d" and use this for computing the minimal change. It would also have to know that certain features are easier to change than others. For instance, if it has only ever seen the word "Xavier" in the training data as an NNP, then it could also suggest that if the word were "Xavier" instead of "Fred" than this error would not have happened. But this is sort of silly, because it gives us no information. (I'm working under the assumption that we want to do this so that we can add/remove features to our model to help correct for errors [on development data :P].)

It seems like neither of these problems is insurmountable. Indeed, just looking at something like feature frequency across the entire training set would give you some sense of which features are easy to change, as well as which ones are highly correlated. (I guess you could even do this on unlabeled data.)

I feel like it's possible to create a methodology for doing this for a specific problem (eg., NE identification), but I'd really like to see some work on a more generic framework that can be applied to a whole host of problems (why did my MT system make that translation?). Perhaps something already exists and I just haven't seen it.