Lance Fortnow eloquenty states that all he wants for Christmas is a proof that P != NP. This got me to thinking about what I, as an NLPer, would most like for Christmas. Unfortunately, things in AI aren't as clear-cut as things in theory because it's hard to tell if we've succeeded. That said, what one might call a Halbert's Program for NLP is a (set of) techniques for super-human aggregation of information from multiple sources, media and domains specific to a given user. There are several "enabling" technologies that (I believe) are necessary to achieve this goal (those that I actively work on are starred):
- Human-level machine translation models (data in, system out)
- Turn-key structured prediction techniques with lots of features, complex loss functions and hidden variables*
- (Weak?) semantic entailment on raw text
- Personalization that doesn't obviate privacy
- Domain adaptation of arbitrary statistical models with little new annotated data*
- Automatic speech recognition
- Graph, equation and table "understanding"
- Weakly supervised summarization, more complex than extraction*
- Database querying from natural language queries
- Social network analysis
- Plan recognition, opinion and point-of-view analysis
There are doubtless other interesting things and, of course, each of these might have its own set of enabling technologies (and there are also some cross dependencies). One perhaps controversial aspect is that I consider MT to be an enabling technology; this is because I don't necessarily care about reading Chinese documents -- I care about the information in the Chinese documents. To me, everything is about getting information, regardless of the source.