Dual Decomposition Inference for Graphical Models over Strings

@inproceedings{Peng2015DualDI,
  title={Dual Decomposition Inference for Graphical Models over Strings},
  author={Nanyun Peng and Ryan Cotterell and Jason Eisner},
  booktitle={EMNLP},
  year={2015}
}
We investigate dual decomposition for joint MAP inference of many strings. Given an arbitrary graphical model, we decompose it into small acyclic sub-models, whose MAP configurations can be found by finite-state composition and dynamic programming. We force the solutions of these subproblems to agree on overlapping variables, by tuning Lagrange multipliers for an adaptively expanding set of variable-lengthn-gram count features. This is the first inference method for arbitrary graphical models… CONTINUE READING

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