Dual decomposition for parsing with non-projective head automata

Authors:
Terry Koo;Alexander M. Rush;Michael Collins;Tommi Jaakkola;David Sontag
Affiliations:
MIT CSAIL, Cambridge, MA;MIT CSAIL, Cambridge, MA;MIT CSAIL, Cambridge, MA;MIT CSAIL, Cambridge, MA;MIT CSAIL, Cambridge, MA
Venue:
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Year:
2010

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Abstract

This paper introduces algorithms for non-projective parsing based on dual decomposition. We focus on parsing algorithms for non-projective head automata, a generalization of head-automata models to non-projective structures. The dual decomposition algorithms are simple and efficient, relying on standard dynamic programming and minimum spanning tree algorithms. They provably solve an LP relaxation of the non-projective parsing problem. Empirically the LP relaxation is very often tight: for many languages, exact solutions are achieved on over 98% of test sentences. The accuracy of our models is higher than previous work on a broad range of datasets.