Invited Talk      




Invited Talk


Fernando Pereira
The Hedgehog and the Fox:
Language Technology and the Knowledge of Language

The fox knows many things, but the hedgehog knows one big thing. -- Archilochus.

Statistical and machine-learning methods have allowed us to create classifiers, taggers and information extractors that can answer predetermined questions about linguistic material with surprising accuracy. However, we have the strong intuition that language ``understanding'' requires something else, the ability to answer accurately a wide range of questions pertaining to any input. What is the relationship between single-question learners and broader understanding? Information-theoretically, we may characterize language processing tasks by the entropy of their output absent any information about the input, and thus draw a continuum between, say, binary text classification and machine translation.

Linguistic representations can also be understood as codified answers to particular kinds of questions pertaining to linguistic material, with their own degrees of information-theoretic difficulty. From this point of view, the task of the learner is to acquire an accurate procedure for deciding whether a simple sentence follows from a discourse, rather than the more traditional tasks of deciding grammaticality or assigning structural descriptions. Structural descriptions would still play an important role in such a theory, but now as proxies for informational relationships between external linguistic events rather than claims on mental representation.

Can hedgehogs evolve into foxes?

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