In structured prediction problems the goal is to learn a function
that maps input points to structured output labels: for example,
strings, graphs, or trees. These problems are common in many
fields---for example, natural language processing (NLP), computer vision,
and computational biology---and have been the focus of a great deal of
recent research in machine learning.

In this talk I'll describe models for two structured prediction
problems in NLP: parsing and machine translation. Central to both
approaches is a variant of Tree Adjoining Grammar (TAG) (Joshi et al.,
1975), which is computationally efficient, but which also allows the
use of relatively rich syntactic representations. The TAG-based parser
generalizes a powerful class of discriminative models (conditional
random fields) to full syntactic parsing. The TAG-based translation
system makes direct use of syntactic structures in modeling
differences in word order between different languages, and in modeling
the grammaticality of translation output. In both cases we show
improvements over state-of-the-art systems.

This is joint work with Xavier Carreras and Terry Koo.