Having an Impact

Having just completed my thesis, I've been thinking a lot about what directions I should persue, post-graduation. While I'll probably stick with some of the stuff I've been working on in the past, this is also an opportunity to diversify. This opens the question: what are promising areas to work on? One important qualification (though by no means the only one) is impact: I want to work on things that are important, either in the sense that they become tools used by others for a diverse set of problems, or change the way people think about or look at a particular area.

Rather than applying my own model for what work has been important, one can look back at what other people think have been the most influential papers in NLP, as well as what papers have won best paper awards recently (with the caveat that the latter is probably less indicative). The story seems to be that the most impactful papers, at least as measured by this criteria, are either MT papers, parsing papers or machine learning papers. This makes some historical sense: MT is sort of the quintessential NLP application, while parsing is essentially the language-for-language's sake task.

This analysis, unfortunately, puts me in a sticky situation. I've already been doing machine learning stuff, which I hope will turn out to be useful for other people. I'd ideally like to get back into really languagey stuff in the future, and this leaves parsing at MT. I have little interest in working on parsing (nothing against parsing: it's just not my thing, and there are lots of people who can probably do it better than me), so now I'm stuct with MT. MT is, without a doubt, an interesting topic. But the field is so crowded right now, it seems difficult to find a sufficiently large and interesting niche to carve out. I personally think out-of-English translation is promising, especially conjoined with morphological research (pretty much required when going into a highly inflected language), but there are a large number of barriers to entry to this problem (evaluation, my American-esque inability to speak any foreign language sufficiently reliably to do error analysis (except Japanese, but there's so little data there), etc.).

But regardless of my personal interests and limitations, the fact that the field is so dominated by two problems seems unfortunate. There are 44 bins that ACL allowed you to select for your topic this year and while many of them largely overlap, there are many that do not. Why, for instance, does research in discourse, inference, phonology, question answering, semantics, and word sense disambiguation have comparatively little weight in the field as a whole? Why are there six sessions on MT (plus one multilingual), six on parsing (plus two on grammars), but only two on semantics and discourse and one on language modeling at ACL this year? And beyond that, there seem to be lots of interesting problems that no one is working on (or at least that no one publishes at ACL). I do not doubt that these are interesting problems, but it seems that there is a severe skew that may not be healthy to the community.