Spectral Regularization for Max-Margin Sequence Tagging

We frame max-margin learning of latent variable structured prediction models as a convex optimization problem, making use of scoring functions computed by input-output observable operator models. This learning problem can be expressed as an optimization involving a low-rank Hankel matrix that represents the input-output operator model. The direct outcome of our work is a new spectral regularization method for max-margin structured prediction. Our experiments confirm that our proposed regularization framework leads to an effective way of controlling the capacity of structured prediction models.

Authors

• Ariadna Quattoni
• Borja Balle
• Xavier Carreras
• Amir Globerson

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