I'd extend hypothesis #2. We might not have a sufficiently high-quality theory, but we might also not know how to integrate the benefits of the theory we have w

I'd extend hypothesis #2. We might not have a sufficiently high-quality theory, but we might also not know how to integrate the benefits of the theory we have with the benefits of data-driven approaches to NLP like those described in this paper. It's clear to me that data-driven techniques are an essential part of the answer to the problem of language understanding. It's infeasible to design a complete set of rules for understanding natural language with all its exceptions and fuzzy relationships. I'd point to Wittgenstein for a stronger philosophical argument on this. That's not to say that theory has no place in language understanding, but it's not immediately obvious how to combine rule-oriented theory with data-driven ML. There has been work in this area though, and actually several important, state-of-the-art-moving contributions have been inspired by linguistics. My favorite are recursive neural nets, which benefit from the linguistic insight that language has recursive structure. This paper[1], for example, describes a system that combines a dependency parser with a recurrent neural net (gross oversimplification) to yield a state-of-the-art sentence understanding model. [1] - http://arxiv.org/abs/1603.06021 http://arxiv.org/abs/1603.06021