Neural networks provide a powerful tool to model language, but also depart from standard methods of linguistic representation, which usually consist of discrete tag, tree, or graph structures. These structures are useful for a number of reasons: they are more interpretable, and also can be useful in downstream tasks. In this talk, I will discuss models that explicitly incorporate these structures as latent variables, allowing for unsupervised or semi-supervised discovery of interpretable linguistic structure, with applications to part-of-speech and morphological tagging, as well as syntactic and semantic parsing.
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