Preprocessing vs. Model

Back from Sydney now, and despite being a bit jetlagged, I thought I'd begin with an COLING/ACL-related post. There was a paper presented by Ben Wellington that discusses whether current syntax-driven models for translation can capture the sorts of reorderings/alignments observed in hand-aligned data. I'm not looking at the paper, but what I remember from the talk is that something like 5-10% of sentences are missed assuming an ITG-like model, without gapping.

At the end of the talk, Dekai Wu commented that they are aware of this for ITG and that an analysis of where it happens is essentially is "weird" extrapositions that occur in English, such as wh-movement, topical preposing, adverbial movements, etc. Apparently such things occur in roughly 5-10% of sentences (from whatever corpora were being used). This talk was near the end of the conference, so my buffer was near full, but my recollection is that the suggestion (implied or explicit) was that English could (should?) be "repaired" by unextraposing such examples, at which time a model like ITG could be directly applied to nearly all the data.

To me this seems like a strange suggestion. ITG is a very simple, very easy to understand model of translation; more natural, in many regards, than, say, the IBM models. (Okay, maybe in all regards.) One nice thing about the model is that it is largely un-hackish. Given this, the last thing I'd really want to do is to fix English by applying some preprocessing step to it, so that the output could then be fed into this nice pretty model.

There's an obvious generalization to this question, hinted at in the title of this post. It seems that in many problems (translation with ITG being but one example), we can either choose a theoretically clean but maybe not completely appropriate model plus some preprocessing, or a somewhat messier but maybe 99.9% recall model with no preprocessing. I suppose one could argue that this is the long sought after integration of rule-based and statistically-based NLP, but this seems uncompelling, because it's not really an integration. I suppose that I'd almost always prefer the latter sort of solution, but that's not to say the first isn't also useful. For instance, in the ITG case, after identifying cases where the ITG assumption fails, we could try to minimally enhance the ITG model to be able to capture these corner cases.